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Youtube To Mp4 Converter Mac Free

Youtube To Mp4 Converter For Mac Free

Youtube To Mp4 Converter Mac Free

YouTube videos are highly shareable for many users. It is a fantastic way to view information and entertainment online. Saving YouTube videos or converting it to MP4 to watch later however can be a difficult task. So, do you know how to convert YouTube video to MP4? In this article, we recommend 15 online converters and 5 YouTube to MP4 converters for you.

Free Safe YouTube to MP4 Converter. You may open this website to convert YouTube to MP4 safe that impressed you to portable devices for playback in your rest time. This free Converter provides the highest quality MP4 conversion for all users by advanced technology. That's this is the best YouTube to MP4 Converter. YouTubNow can be categorized in one of the free online video converters list wherein the users can simply convert down the mp4 file to YouTube using this converter. This mp4 to YouTube converter allows users to adjust the video quality in 1080p, 144p, 360p to 720p resolutions while maintaining the video quality at the same time. Convert YouTube videos to MP4 in HD and UHD without ads. Use the YouTube video converter brought to you by famous software developer DVDVideoSoft. Step-by-step free methods to convert your YouTube videos to MP4 files. Easily convert YouTube video files without losing quality and play any device like PC, Mac, Android, iPhone without compatibility issues.

Part 1: Top 15 Online YouTube to MP4 Converters

1. OnlineVideoConverter

This YouTube to MP4 converter online is a free converter and downloader with no limits.

Feature:

• Full compatibility with modern browsers.

• Support most online video portals.

• High-speed conversion.

Disadvantage:

• Many ads in the interface.

• The formats it can convert is limited.

How to Use:

Step 1: Find your favorite YouTube video you want to convert, then copy the URL of this video.

Step 2: Open OnlineVideoConverter, and paste the URL in it.

Step 3: Select a format of your choice, such as MP4.

Step 4: Click 'Start' to begin the conversion process. When it finished, you will receive a download link for the converted file.

2. Convert in MP4

This website is a converter which can full your basic needs, just convert YouTube video to MP4.

Feature:

• Work on mobile & consoles.

• Download videos in school.

Disadvantage:

• Some ads in interface.

• Function is basic.

How to Use:

Step 1: Enter your YouTube link in it or paste the link.

Step 2: Click 'Download MP4'.

3. Converto.io

Converto.io is one of the simplest way to download and convert any YouTube video to MP4 online.

Feature:

• MP4 video up to 4K resolution.

• Allow you cut or change video quality by clicking 'Settings' icon.

Disadvantage:

• Some ads in the interface.

• The formats it can be converted is limited.

How to Use:

Step 1: Copy YouTube URL that you want to convert.

Step 2: Paste the URL on Converto.io.

Step 3: Click 'Convert' button.

4. Flvto YouTube Downloader

Flvto YouTube Downloader makes it as easy as possible to convert your favorite YouTube videos to any formats including MP4.

Feature:

• Support more than 20 languages.

• Match OS, Linux, and Windows.

• Download videos from any website.

Disadvantage:

• Just 5 formats can be converted.

How to Use:

Step 1: Enter the link of the media.

Step 2: Choose the format and click 'Convert to' button.

5. Convert2MP3

Except output quality, Convert2MP3 can be one of the best YouTube to MP4 converter free online.

Feature:

• No registration needed.

• Easy to use.

• Allow you search in this page.

Disadvantage:

• The output quality is not very high.

How to Use:

Step 1: Search for a video.

Step 2: Copy the link of video and then paste it in the first box.

Step 3: Select the format and press 'Convert'.

6. Clip Converter

Clip Converter is a free online media conversion application to help you convert YouTube video online.

Feature:

• Clear interface.

• Support nearly all audio or video URL.

• Free and fast.

Disadvantage:

• Conversion formats are limited.

How to Use:

Step 1: Copy the link and paste the URL at 'Video URL to Download'.

Step 2: Press 'Continue'. Select the format.

7. 2conv.com

It may be the fasted and safest tool to convert YouTube to MP4 online.

Feature:

• Fast download speed.

• Run smoothly on Mac, Windows and Linux.

• Work with most of browsers.

Disadvantage:

• Ads in the interface.

• Just 5 formats can be converted.

How to Use:

Step 1: Copy the link of your wanted video. Paste the URL into the input field.

Step 2: Hit 'Convert to MP4' button and wait a few seconds.

8. Fetchy

Fetchy is a great YouTube to MP4 converter online allows you to search, watch and convert videos all in one site.

Feature:

• Search, watch and convert video all in one site.

• Allow you browse YouTube videos from within the site.

Disadvantage:

• The background is so black that the introduce is hard to read.

• The converted formats is limited.

How to Use:

Step 1: Copy the YouTube link and paste it in this website.

Step 2: After converting, you can download this MP4.

9. YtMp3

Use YtMp3 can easily convert videos to MP3 or MP4 files and download them for free.

Feature:

• Clear interface and easy to use.

• Fast speed.

• No ads.

Disadvantage:

• Only convert videos up to a length of 2 hours.

• Just can be converted to MP3 or MP4.

How to Use:

Step 1: Insert a valid video URL.

Step 2: Paste the URL on Converto.io.

Step 3: Choose your format and click 'Download'.

10. Catch-video

This YouTube to MP4 converter online can display all the formats converted for this video.

Feature:

• Display all the available links for the video for you.

• Choose video quality freely.

Disadvantage:

• Can't choose your wanted format.

How to Use:

Step 1: Select the video quality. Paste the URL and press 'Catch!'.

Step 2: Choose your wanted format from all available links and download it.

11. Yonverter

You can easily convert your favorite YouTube video to MP4 with the assistant of Yonverter.

Feature:

• Easy to use.

• No unhappy advertise.

• Allow you download videos on your phone.

Disadvantage:

• The interface is not very clear.

• Just 3 formats you can choose.

How to Use:

Step 1: Paste the URL in this website.

Step 2: Choose the format. Then click 'convert & download'.

12. KeepVid

Youtube To Mp4 Converter For Mac Free

KeepVid is a perfect YouTube to MP4 converter online to help you convert videos on web be your personal resources.

Feature:

• Allow you download videos from more than 1000 video websites.

• Convert your downloaded YouTube videos to more than 150 video or audio formats.

Disadvantage:

Youtube To Mp4 Converter Mac Free

• Some ads which will influence users.

How to Use:

Step 1: Paste the URL and click 'Download'.

Step 2: Choose the quality and format, then begin to download.

13. YouTube Video Converter

This application can give you many service to use and help you convert YouTube video to MP4 in high quality.

Feature:

• Support links from most video websites.

• No limitation on conversion and downloads.

Disadvantage:

• Some ads in the interface.

• The formats are not full.

How to Use:

Step 1: Copy the paste the video link.

Step 2: Select format and click 'Start'.

14. Online Convert

This MP4 converter helps you easily convert files to MP4 format.

Feature:

• 3 methods to input the video you want to convert.

• Support you change optional settings.

Disadvantage:

• Some ads in the interface.

• Just can be converted to MP4.

How to Use:

Step 1: Upload your video or enter the URL or select a file from cloud storage.

Step 2: Set optional settings. Click 'Convert file'.

15. YouTube MP4 Converter

This YouTube to MP4 converter does good job in converting video to MP4.

Feature:

• Don't need any software.

• Easy to use.

Disadvantage:

• Just can be converted to MP3 and MP4.

How to Use:

Step 1: Copy and paste the URL.

Step 2: Click 'Download'.

Part 2: 5 best software to convert YouTube to MP4 video

1. Video Converter Ultimate

Video Converter Ultimate is the most comprehensive YouTube to MP4 converter. With this application at hand, you can easily enjoy audios and videos on your own devices. What's more, it supports more than 200 kinds of formats. Just get this application and enjoy high speed of converting and high quality of videos.

Feature:

• Convert any videos or audios to any conceivable formats.

• Download 4K YouTube videos and paly on portable devices.

• Convert DVD movies to various videos and audios.

• Allow you adjust video's settings.

• Fast converting speed.

Disadvantage:

• Not free.

How to Use:

Step 1: Download and run Video Converter Ultimate on your computer.

Step 2: Click 'Add File' button and add the video you intend to convert.

Step 3: Check the video file and click Edit to the edit window. Adjust the effect if you want.

Step 4: Click Profile and choose your wanted format. Then define the output folder where converted video can be saved automatically.

Step 5: Click 'Convert' button and just wait for it.

2. FonePaw Video Converter Ultimate

This software is one of the best YouTube to MP4 converters. It costs short time and save all of your favorite videos on your computer.

Spss clementine 12.0. Feature:

• High-speed conversion.

• Play any video or audio handily.

• Enhance video quality.

Disadvantage:

• More complicated than online converter.

How to Use:

Step 1: Add YouTube videos to the application after installing and running it.

Step 2: Choose MP4 as the output format.

Step 3: Select the output folder and convert YouTube to MP4.

3. Wondershare Video Converter

This converter is a handy and very strong YouTube to MP4 converter, what's more, the quality of output video is much strong than some of the free or paid tools.

Feature:

• Convert video to over 1000 formats.

• Easy-to-use editor.

• Enjoy it without Internet connection.

Disadvantage:

• Not free.

How to Use:

Step 1: Launch this program and add your YouTube video.

Step 2: Choose your wanted format and convert the media files.

4. WinX YouTube Downloader

WinX lets you fast download content from YouTube and other more than 300 sites. No wanted ads, and absolutely free.

Feature:

• Download videos or music in batch.

• Flexible in size and quality.

• Safe and clean installation.

Disadvantage:

• Just support Windows 10 and lower

How to Use:

Step 1: Download and launch it.

Step 2: Add your YouTube videos you want to convert.

Step 2: Choose format like MP4. Start converting.

5. Video Converter for Mac

It is an easy and great tool, which can satisfy all Mac users' needs on converting YouTube video to MP4.

Feature:

• Convert videos with the highest possible quality output.

• Regain space by compressing large media flies.

• Supported by advanced audio and video sync technology.

Disadvantage:

• You can try it for free, but it is not free if you want to download.

How to Use:

Step 1: After downloading and launching this software, drop your videos to it.

Step 2: Click the format icon to choose your wanted format.

Step 3: You can adjust videos' effect by clicking Advanced Settings. Last click and begin this conversion.

Part 3: FAQs of YouTube to MP4 Converter

1. What is the best YouTube to MP4 Converter with no virus?

AnyMP4 Video Converter Ultimate is the best YouTube to MP4 Converter that allows you to convert any YouTube video to MP4. It is totally secure and clean to download to your Windows or Mac.

2. Can you upload MP4 to YouTube?

MP4 is a container format that can store multiple video, audio and subtitle data and more. YouTube is only compatible with MPEG-4/H.264 video. When MP4 video that you want to upload contains YouTube unsupported codecs, YouTube will reject your video file.

3. What is the best video format to use for YouTube?

It's stated by YouTube that MP4 is the best format for uploading. It provides better quality than FLV. And it is not as heavy as AVI, WMV format which could lose the video quality.

4. Can I convert YouTube to MP3?

Yes, you can convert your YouTube videos to MP3. To turn a YouTube to MP3, you just need a converter tool. AnyMP4 Video Converter Ultimate is an easy-to-use converter tool that allows you to extract MP3 audio format from a YouTube video.

Conclusion

We mentioned 15 best YouTube to MP4 converter online and 5 best YouTube to MP4 converter above for you. Each of them may have some disadvantages, but they also can help you convert your favorite YouTube to MP4. In addition, all of them are easy to use, after only two or three steps you can get the converted MP4 video. Choose your suitable solution to convert YouTube video to MP4 according your needs. Hope these tools and tips can help you.

Our YouTube to mp4 converter is the easiest way to convert YouTube videos to mp4 files online and download them for free. The videos will be always converted in the highest available quality. Unlike other websites, you can also convert videos that are blocked in your country. Also, all conversion services will be done on our servers so your browser will not be put under any pressure and you may still be able to browse other sites while converting videos. Our converter works on all type of devices (computer, tablet and mobile).

There is no registration or software needed to use the converter. The service is free. With the use of our service you are accepting our Terms of use.

How to start? Just copy the YouTube video URL from youtube.com, then paste it in our converter and click „Convert”. Then the conversion will start and it should finish in just a few minutes. As soon as the conversion is finished you will be able to click the „Download” button to start the download.

That's all! Have fun and enjoy!

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precisionload375 · 3 years ago

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Mac Catalina Vmware

MacOS Catalina 10.15.6 seems to have introduced a bug that causes system crashes, especially when using virtualization software such as VirtualBox or VMware. Users of virtualization software have. Spss clementine 12.0. That’s all about 6 STEP: Install macOS Catalina 10.15 on VMware on Windows PC. I hope you have gotten something from this article, again if you faced any kind of problem shares your comment in the comment section. After I launched my VM the first time in Catalina and saw this issue (black screen, but active preview), I found this post, and did see VMWare Fusion in the list of apps for Screen Recording. I checked the box, and now it's working fine for me. Also, I never recall seeing a prompt to add the permission, either. 👉How to install macOS Catalina using VMware WorkStation on Microsoft Windows 10.👉Using macOS on VMware without real Mac device.🌍VMware Unlocker: https://g.

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precisionload375 · 4 years ago

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Adobe Zii 2019 Reddit

Adobe Zii Patcher 2019 Reddit

Photoshop portable free. After the latest releases of Adobe Creative Cloud 2019 updates that includes all adobe software products, here is Adobe Zii 4.0.4 For Adobe 2019 that automatically Patch and Crack Adobe CC 2019 with ease.

Adobe Zii Patcher 4.0.4 was created by the T.N.T. And is available for MAC OS X 10.8 or higher only. Python unrar. You can Download Adobe Zii Universal Patcher For MAC Free from the link below.

How to use Adobe Zii Patcher 4.0.4 To Activate Adobe CC 2019 ?

Adobe Zii CC2019 4.4.2 macOS Team: TNT 9MB Adobe Zii is a small and great utility which helps you forget about all issues with the registration of all Adobe existing products. Adobe universal patcher reddit. Oct 31, 2019 — Download Adobe Zii 4.5.0 CC2019 Universal Patcher. Adobe Zii is a small and great utility which helps you forget. Adobe Zii, first released in 2016 by the TNT project is a manual bug exploiter for the Adobe CC suite. This subreddit has the sole purpose of clarifying doubts related to the service and promoting a constructive debate about its functionalities.

Disable the Internet connection

Install Adobe packages in Trial version through Adobe Creative Cloud

Open and accept the trial version while the program is running, then close the software immediately

After that, Open Adobe Zii 2019

Once the Activator is open click on ‘Patch’ or transfer the adobe application to be edited in the Patcher

That’s all, Enjoy!

What’s New In 4.0.4 Version :

Adobe Zii Patcher 2019 Reddit

(NEW) Adobe After Effects CC 2019

(NEW) Adobe Media Encoder CC 2019

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precisionload375 · 4 years ago

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Spss Clementine 12.0

How To Download Spss Clementine 12.0

Spss Clementine 12.0 Free

SPSS Clementine 12.0 is a software application. This page holds details on how to uninstall it from your computer. The Windows release was createdSPSS Clementine 12.0 is an application released by SPSS Inc. Some computer users try to remove it. Sometimes this is hard because performing this. SPSS Clementine (clementine.exe) latest version 12.0.4.14, IBM® SPSS® Modeler is a powerful, versatile data and text analytics workbench that helps you build accurate predictive models quickly and intuitively, without programming. Easily visualize the data mining process, using IBM SPSS Modeler's intuitive graphical interface. Clementine 1.0 – June 1994 by ISL; Clementine 5.1 – Jan 2000 Clementine 12.0 – Jan 2008 PASW Modeler 13 (formerly Clementine) – April 2009 IBM SPSS Modeler 14.0 – 2010 IBM SPSS Modeler 14.2 – 2011 IBM SPSS Modeler 15.0 – June 2012 IBM SPSS Modeler 16.0 – December 2013 Product history. IBM SPSS Statistics - Essentials for Python 21.0. SPSS, an IBM Company. SPSS Clementine 12.0.4.14. Jan 14, 2008 With Clementine 12.0, SPSS Goes Deep. Analytical software provider SPSS announced today the latest upgrade of its data-mining workbench Clementine 12.0 and an accompanying Text Mining software solution. The release aims to provide significantly more advanced analytical and predictive power, more comprehensible and visually rich presentations.

Published online 2014 Jan 31. doi: 10.4258/hir.2014.20.1.30

PMID: 24627816

This article has been cited by other articles in PMC.

Abstract

Objectives

The purpose of this study was to design a prediction model that explains the characteristics of elderly adults at risk of malnutrition.

Methods

Data were obtained from a large data set, 2008 Korean Elderly Survey, in which the data of 15,146 subjects were entered. With nutritional status a target variable, the input variables included the demographic and socioeconomic status of participants. The data were analyzed by using the SPSS Clementine 12.0 program's feature selection node to select meaningful variables.

Results

Among the C5.0, C&R Tree, QUEST, and CHAID models, the highest predictability was reported by C&R Tree with the accuracy rate of 77.1%. The presence of more than two comorbidities, living alone status, having severe difficulty in daily activities, and lower perceived economic status were identified as risk factors of malnutrition in elderly.

Conclusions

A reliable decision support model was designed to provide accurate information regarding the characteristics of elderly individuals with malnutrition. The findings demonstrated the good feasibility of data mining when used for a large community data set and its value in assisting health professionals and local decision makers to come up with effective strategies for achieving public health goals.

Keywords: Decision Trees, Data Mining, Malnutrition, Aged, Community

I. Introduction

Data mining is a recently developed technological methodology that has been used intensively and extensively in many fields. It is defined as the process of discovering previously unknown patterns or trends from stored data, building predictive models based on that information. Compared with the past healthcare system, there has been an ever increasing amount of data generated by current healthcare settings. The amount of data generated in current healthcare settings is becoming too large and complex to be analyzed by traditional statistical methods. Due to its good applicability to any field of study, data mining has gradually gained popularity in almost all areas of healthcare (1). However, data mining has been used mostly to analyze disease-focused clinical data rather than community data gathered for primary care providers in Korea. The importance of accessing community health status has been widely recognized throughout the nation and data mining can empower local decision makers with a clear methodology for organizing and interpreting healthcare data ().

Data mining has been recognized as a solution to handle these large amounts of complex data, transforming these data into meaningful information for the decision making process of health professionals. A comprehensive community health profile plays an important role in the development, refining and monitoring of long-term strategies designed to improve the overall health status of the population across the nation. Such information obtained at the community level can be used to ensure that the best resource allocation choices are made to improve community health (3). Along with Korean Elderly Survey, a number of national representative studies, such as the Korean Longitudinal Study of Ageing and the Korean Retirement and Income Study, have recently been conducted in Korea (,5). Currently, more healthcare data are available at the community level, enabling health professionals to make a transition from the previous disease- and treatment-focused strategy to a health- and prevention-focused strategy. This also clarifies the public health role, which should be supported by unbiased data that accurately reflect a community's health status, needs, and resource allocation (6).

The proportion of elderly population in Korea reached 11.8% in 2012 which is expected to increase further accounting for 24.3% in 2030 and 37.4% by 2050 (7). The growing elderly population has a huge impact on the healthcare system, and malnutrition is a common health problems found frequently among the elderly (8). The leading causes of malnutrition include several individual factors, such as poor oral health, loss of vision and hearing, dementia, impaired mobility, and pain. Malnutrition is both a cause and consequence of many geriatric diseases that accounts for a significant proportion of national medical spending (). Nutritional status has a great influence on the immune system of the human body; the lower immune response induced by malnutrition eventually puts elderly individuals at a high risk of infection, increasing the risk of mortality (10). A rapid and precise diagnostic method that supports clinical decision enables the earlier identification of the elderly at risk of malnutrition. This will eventually prevent negative outcomes caused by poor nutritional status, resulting in a substantial reduction in healthcare cost spent on the elderly population (). Most studies to date have conducted research on small samples obtained from local hospitals or specific communities using traditional statistical analysis, which may not be sufficient for discovering comprehensive knowledge (). In addition, the limited number of variables analyzed in those studies could not provide integrative understanding of elderly malnutrition.

Data mining is known to be the best method in terms of the cost estimation, disease diagnosis, prediction of prognosis, and the discovery of hidden patterns in the healthcare system with the use of large health database (13). A decision tree model presented in this study was developed from a combination of decision tree approaches and statistical analysis. Data mining provides information that can be the used in the analysis of risk factors for certain types of diseases. Contrasting the characteristics of ill patients with those of healthy people to find the patterns related to the occurrence of disease has been a common practice in evidence-based medicine (). The purposes of the study were to identify the best modeling method among data mining tools by testing the discriminatory power of individual models and to identify the patterns related to the occurrence of malnutrition in elderly. Finding patterns for the related characteristics of risky conditions that threaten the health of the community may contribute to the achievement of public health goals with effective disease prevention strategies. By identifying the associated characteristics of older adults who are more likely to be malnourished in community settings, the decision tree model allows health professionals and decision makers to intervene earlier to allocate adequate resource for nutritional support, improving the overall health of the community.

II. Methods

1. Subjects

A secondary analysis of a large community data set from the 2008 national study of Korean elderly was conducted to provide accurate information regarding the characteristics of the elderly at risk of malnutrition in Korea. The Institutional Review Board gave prior approval to the study. The sample consisted of 15,146 adults over 60 years old and community dwelling. The ten items of the 'Determine Your Nutritional Health Checklist' were used to access dietary intake and meal patterns. The tool was developed by the Nutritional Screening Initiative to screen nutritional risk in the elderly population. A summed score of 0-2 indicates good nutritional status, 3-5 moderate risks, and 6 or more high risk (). In this study, elderly people with summed scores of 3 or more were classified as being at risk of malnutrition as they pose problems for malnutrition.

2. Decision Tree Models

Figure 1 presents the data mining process that started with the selection of variables. The target variable was malnutrition, and the 52 input variables covered all aspects of elderly malnutrition, including general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment (Figure 2). The ratio between the training data and test data was 7:3 by random division of the full data set to which four tree-structured models, including C5.0, CHAID, QUEST, and classification and regression (C&R) Tree, were applied. In an assessment of model performance, analysis node was used to calculate the percentage of correct and wrong classification of each model, and then the correct classification rates of the models were compared to select the best model. In this study, the C&R Tree showed the best predictive performance, and it was selected to draw the final decision support tree (16). A decision tree is a visual representation of obtained knowledge using a tree form in which each branch represents an output of the test. This decision tree enables easy understanding and interpretation of data as the nodes and branches are organized hierarchically. It generates reliable knowledge, promoting accuracy in clinical decision-making (). The C&R Tree node is a tree-based classification and prediction method. The C&R Tree approach provides a very simple representation that displays accumulated knowledge well. This method allows the tree to grow large before pruning, which results in smaller trees, and this ensures better cross-validation properties of decision tree modeling (18). The recursive partitioning process splits the records into segments with similar output field values, and two subgroups are defined by the first split. The split ends when one of stopping criteria is triggered ().

Process of data mining.

Input variables and feature selection.

3. Statistical Analysis

The default experimental parameters of Clementine ver. 12.0 (SPSS Inc., Chicago, IL, USA) were utilized to construct the decision tree model with the usage of the C&R Tree component in this study. SPSS ver. 12.0 for Windows (SPSS Inc.) was used to perform the statistical analysis in which important variables were chosen using the feature selection method. Among Pearson, Likelihood ratio, Cramer's V, and Lambda-a, Pearson correlation analysis was selected to prioritize the meaningful variables (20).

III. Results

1. Demographic Characteristics of the Subjects

A total of 15,146 elderly subjects were included in this study, and the sample consisted of 6,185 males (40.8%) and 8,961 females (59.2%). Of the total participants, 7,769 (51.3%) had normal nutritional status while 7,377 (48.7%) had poor nutritional status. Compared with elderly subjects showing good nutritional status, the mean age of elderly subjects at risk of malnutrition was higher (Table 1). About three-fourths of older people living alone were categorized as having poor nutritional status, and there was a greater proportion of elderly subjects with good nutritional status in the higher monthly income group. Apart from 54 participants (0.4%), the majority of participants had been married at least once. While malnourished elderly subjects comprised about one third of the married group, the proportion of malnourished elderly subjects was much greater in the separated or living alone groups, accounting for 73.5% and 65.2%, respectively.

Table 1

2. Selecting Important Attributes

As a result of the feature selection process, 48 important attributes were identified to be important variables of a total of 52 input variables shown in Figure 2. Seven variables including chewing ability, level of life satisfaction, depression status, health status, number of diagnosed diseases, difficulties in daily activities caused by pain, subjective economic state, and monthly income were identified to be most significant variables.

3. Comparing Predictability of the Models using Analysis Node

The highest percentage of correct prediction in training data was reported in C5.0; however, the C&R Tree model showed relatively higher percentages of correct prediction in both training and test data with 78.10% and 80.95%, respectively (Table 2).

Table 2

Predictive performance according to modelling methods

4. Modeling of the Data using C&R Tree

Figure 2 presents seven finalized input variables that were selected during feature selection. The final decision support model was designed using the C&R Tree algorithm and is shown in Figure 3.

Decision tree model based on C&R Tree.

In this study, a total of six rules were identified to be associated with the risk of malnutrition in the elderly. The six decision rules were the following: 1) good and very good chewing ability, depressed, level of life satisfaction less than 2.89; 2) good and very good chewing ability, depressed, level of life satisfaction above 2.89, severe difficulty in daily activity caused by pain; 3) low chewing ability, living with spouse or children; low chewing ability, living with spouse or children, depressed; 5) low chewing ability, living with spouse or children, not depressed, number of disease above 2.5; 6) low chewing ability, living with spouse or children, not depressed, number of disease less than 2.5, subjective income status is poor.

5. Variables in the Final Modeling

The traditional statistical method and chi-square test were used to find out whether there are significant differences between the elderly at risk of malnutrition and normal subjects in the seven final variables identified by the C&R Tree algorithm. All seven variables showed statistical significance with a p-value of <0.05 (Table 3). Chewing ability, in particular, showed the biggest differences between well-nourished participants and malnourished participants. The majority of participants with low to moderate chewing ability had poor nutrition status (84.1% and 70.2%, respectively) while eight out of ten participants with either good or very good chewing ability were well-nourished. In addition higher proportions of individuals with depression, low life satisfaction, many difficulties in daily activities and poorly perceived economic status were found to be at risk of malnutrition.

Table 3

Statistical analysis to variables used final modeling

Values are presented as mean ± standard deviation or number (%).

IV. Discussion

This study explored the applicability of a prediction model using data mining of a large community data set. To improve the accuracy of knowledge regarding the characteristics of the elderly at risk of malnutrition, we identified significant rules during the process of data mining. Elderly subjects at risk of malnutrition were compared to normal and well-nourished elderly subjects to find the patterns associated with the occurrence of malnutrition in the elderly population. A tree-structured decision model was designed, and its potential application to large amounts of public health data was examined. Malnutrition is a commonly reported condition among the elderly, and it is considered to be both a cause and consequence of many age-related diseases. There has been growing concern over poor nutrition status in Korea as we are becoming an aging population. Therefore, this study was carried out to find patterns related to malnutrition in the elderly. Using the C&R Tree model, a well-designed prediction model was developed, which showed good performance in finding associated rules (18). The C&R Tree uses a recursive partitioning method that provides a very simple representation that displays accumulated knowledge with a well-organized structure. To predict continuous dependent variables (regression) and categorical predictor variables (classification), the C&R Tree builds a classification and regression tree (). Compared with other classification technique used for classification or regression of problems, there are many benefits that can be obtained using the C&R Tree (21). The simplicity of the C&R Tree enables clinicians to make rapid classification of new clinical observations. Moreover, when there is a little prior knowledge, tree methods are known to be well suited for data mining tasks for data from healthcare settings ().

In this study, seven variables and six rules for the prediction of malnutrition risk in elderly subjects were identified. Those associated rules provide information regarding co-occurrence and relationships between risk factors that may increase the probability of elderly suffering from malnutrition. According to the rules identified by the decision tree model, elderly people living alone with low chewing ability, depression, low life satisfaction, comorbidity, functional limitation and subjective economic issues are more likely to be malnourished. Findings are consistent with previous studies conducted to identify patient factors associated with the risk of malnutrition that was strongly associated with age-related changes of older adults. Elderly people with swallowing difficulties due to oral problems, such as absence of teeth, poor oral hygiene, and gum infections, have a decreased food intake and are vulnerable to malnutrition (). In addition, several studies found that along with functional changes, social changes in elderly people's lives (loss, changed family structure, and decreased income) may result in depression and reduced life satisfaction, increasing the risk of being malnourished ().

The final results of the tree method demonstrated the usability of large public health data sets with good feasibility of decision model in the classification of elderly with malnutrition. Through repeated testing and refinement of data mining and the C&R Tree in particular, it is anticipated that new knowledge will be discovered by more sophisticated analysis of healthcare data at the community level (13). As a result of this study, a reliable decision support model was designed that provides accurate information regarding the characteristics of the elderly with malnutrition. The algorithm used to construct the decision tree showed high accuracy, and it is expected to facilitate the discovery of discriminatory knowledge for the targeted problem. The C&R Tree, which was based on the C&R Tree method, provided excellent discrimination of the characteristics associated with malnutrition in the elderly. This decision tree can be utilized to identify community residing elderly individuals who are at high risk of malnutrition; this will eventually contribute to significantly reducing healthcare costs spent on treating malnutrition and its complications in the elderly.

Acknowledgments

This work was supported by Basic Science Research Program through the National Research Foundation of Korea grant funded by the Korean government (No. 2010-0024922).

Footnotes

No potential conflict of interest relevant to this article was reported.

References

1. Abbott PA. Knowledge discovery in large data sets: a primer for data mining applications in health care. In: Ball MJ, Hannah KJ, Newbold SK, Douglas JV, editors. Nursing informatics: where caring and technology meet. New York: Springer; 2000. pp. 139–148. (Google Scholar)

2. Studnicki J, Hevner AR, Berndt DJ, Luther SL. Comparing alternative methods for composing community peer groups: a data warehouse application. J Public Health Manag Pract. 2001;7(6):87–95. (PubMed) (Google Scholar)

3. Berndt DJ, Hevner AR, Studnicki J. Data warehouse dissemination strategies for community health assessments. Upgrade. 2001;2(1):48–54.(Google Scholar)

4. Jang SN, Cho SI, Chang J, Boo K, Shin HG, Lee H, et al. Employment status and depressive symptoms in Koreans: results from a baseline survey of the Korean Longitudinal Study of Aging. J Gerontol B Psychol Sci Soc Sci. 2009;64(5):677–683. (PubMed) (Google Scholar)

How To Download Spss Clementine 12.0

5. Kim HS. Income in old ages and role of children; Proceedings of the 1st Korean Retirement and Income Study (KReIS) Conference; 2008 Jun 24; Seoul, Korea. (Google Scholar)

6. Cropper S. Collaborative working and the issue of sustainability. In: Huxham C, editor. Creating collaborative advantage. London: SAGE Publications; 1996. pp. 80–100. (Google Scholar)

7. Korea Ministry for Health and Welfare. 2008 Korean Elderly Survey. Seoul, Korea: Ministry for Health and Welfare; 2009. (Google Scholar)

8. Alzheimer's Society. Food for thought. London: Alzheimer's Society; 2012. (Google Scholar)

9. Fitzpatrick J. Oral health care needs of dependent older people: responsibilities of nurses and care staff. J Adv Nurs. 2000;32(6):1325–1332. (PubMed) (Google Scholar)

10. National Collaborating Centre for Acute Care. Nutrition support for adults: oral nutrition support, enteral tube feeding and parenteral nutrition: methods, evidence & guidance. London: National Collaborating Centre for Acute Care; 2006. (Google Scholar)

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11. Jones JM. The methodology of nutritional screening and assessment tools. J Hum Nutr Diet. 2002;15(1):59–71. (PubMed) (Google Scholar)

12. Park M, Choi S, Shin AM, Koo CH. Analysis of the characteristics of the older adults with depression using data mining decision tree analysis. J Korean Acad Nurs. 2013;43(1):1–10. (PubMed) (Google Scholar)

13. Ahn SY. ADL, IADL and cognition of elders living alone. J Korean Gerontol Nurs. 2007;9(1):68–75.(Google Scholar)

14. Li J, Fu AW, Fahey P. Efficient discovery of risk patterns in medical data. Artif Intell Med. 2009;45(1):77–89. (PubMed) (Google Scholar)

15. Barrocas A, White JV, Gomez C, Smithwick L. Assessing health status in the elderly: the nutrition screening initiative. J Health Care Poor Underserved. 1996;7(3):210–218. (PubMed) (Google Scholar)

16. Fayyad UM, Piatetsky-Shapiro G, Smyth P. Advances in knowledge discovery and data mining. Menlo Park (CA): AAAI Press; 1996. (Google Scholar)

17. Austin PC, Tu JV, Lee DS. Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure. J Clin Epidemiol. 2010;63(10):1145–1155. (PubMed) (Google Scholar)

18. García S, Fernández A, Herrera F. Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems. Appl Soft Comput. 2009;9(4):1304–1314.(Google Scholar)

19. Hallick JN. Analytics and the data warehouse. Health Manag Technol. 2001;22(6):24–25. (PubMed) (Google Scholar)

20. Huh J, Jeong KS, Huh SH, Choi HK. Clementine 7 manual. Seoul, Korea: Data Solution; 2003. (Google Scholar)

21. Huh MH, Lee YG. Data mining modeling and case. 2nd ed. Seoul: Hannarae; 2008. (Google Scholar)

22. Koh HC, Leong SK. Data mining applications in the context of casemix. Ann Acad Med Singapore. 2001;30(4 Suppl):41–49. (PubMed) (Google Scholar)

23. Sheiham A, Steele J. Does the condition of the mouth and teeth affect the ability to eat certain foods, nutrient and dietary intake and nutritional status amongst older people? Public Health Nutr. 2001;4(3):797–803. (PubMed) (Google Scholar)

24. Vanderwee K, Clays E, Bocquaert I, Gobert M, Folens B, Defloor T. Malnutrition and associated factors in elderly hospital patients: a Belgian cross-sectional, multi-centre study. Clin Nutr. 2010;29(4):469–476. (PubMed) (Google Scholar)

Articles from Healthcare Informatics Research are provided here courtesy of Korean Society of Medical Informatics

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Python Unrar

A ctypes wrapper for UnRAR library, plus a rarfile module on top of it. matiasb/python-unrar. Here are the examples of the python api unrar.RARProcessFileW taken from open source projects. By voting up you can indicate which examples are most useful and appropriate. Unrar All Files and Directories. We can use e option in order to extract all files and directories inside a rar file. In this example, we will extract contents of ans.rar.r is used to extract recursively which means all sub files and directories will be extracted too. $ unrar e -r ans.rar.

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The RAR file format is a common archive and compression standard. This moduleprovides tools to read and list a RAR file.

This module is based on the UnRAR library (provided by RARLAB) through a ctypes wrapper, and inspired on Python’s ZipFile.

The module defines the following items:

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exception rarfile.BadRarFile¶

The error raised for bad RAR files.

class rarfile.RarFile¶

The class for reading RAR files. See sectionRarFile Objects for constructor details.

class rarfile.RarInfo¶

Class used to represent information about a member of an archive. Instancesof this class are returned by the getinfo() and infolist()methods of RarFile objects. Most users of the rarfile modulewill not need to create these, but only use those created by thismodule. header should be a RARHeaderDataEx instance as returned by unrarlib; the fields are described in sectionRarInfo Objects.

rarfile.is_rarfile(filename)¶

Returns True if filename is a valid RAR file based on its magic number,otherwise returns False.

See also

RARLAB

Official RAR site.

RAR addons

RAR addons where you can download UnRAR library sources.

RarFile Objects¶

class rarfile.RarFile(file(, mode='r'))

Open a RAR file, where file should be a path to a file (a string). The mode parameter should be 'r' to read an existingfile (only allowed mode at the moment).

RarFile.getinfo(name)¶

Return a RarInfo object with information about the archive membername. Calling getinfo() for a name not currently contained in thearchive will raise a KeyError.

RarFile.infolist()¶

Return a list containing a RarInfo object for each member of thearchive. The objects are in the same order as their entries in the actual RARfile on disk if an existing archive was opened.

RarFile.namelist()¶

Return a list of archive members by name.

RarFile.open(member(, pwd))¶

Extract a member from the archive as a file-like object(see Python’s io.BytesIO).member is the name of the file in the archive, or a RarInfo object.pwd is the password used for encrypted files.

RarFile.read(name(, pwd))¶

Return the bytes of the file member in the archive. member is the name of thefile in the archive, or a RarInfo object. pwd is the password used for encrypted files and, if specified, it will override the default password set with setpassword().

New in version 0.3.

RarFile.extract(member, path=None, pwd=None)¶

Extract a member from the archive to the current working directory; membermust be its full name or a RarInfo object). Its file information isextracted as accurately as possible. path specifies a different directoryto extract to. member can be a filename or a RarInfo object.pwd is the password used for encrypted files.

RarFile.extractall(path=None, members=None, pwd=None)¶

Extract all members from the archive to the current working directory. pathspecifies a different directory to extract to. members is optional and mustbe a subset of the list returned by namelist(). pwd is the passwordused for encrypted files.

Warning

Never extract archives from untrusted sources without prior inspection.It is possible that files are created outside of path, e.g. membersthat have absolute filenames starting with '/' or filenames with twodots '.'.

RarFile.printdir()¶

Print a table of contents for the archive to sys.stdout.

RarFile.setpassword(pwd)¶

Set pwd as default password to extract encrypted files.

RarFile.testrar()¶

Read all the files in the archive and check their CRC’s and file headers.Return the name of the first bad file, or else return None.

The following data attribute is also available:

RarFile.comment¶

Python Rarfile

The comment text associated with the RAR file, if any.

RarInfo Objects¶

Instances of the RarInfo class are returned by the getinfo() andinfolist() methods of RarFile objects. Each object storesinformation about a single member of the RAR archive.

Instances have the following attributes:

RarInfo.filename¶

Name of the file in the archive.

RarInfo.date_time¶

The time and date of the last modification to the archive member. This is atuple of six values:

IndexValue0Year (>= 1980)1Month (one-based)2Day of month (one-based)3Hours (zero-based)4Minutes (zero-based)5Seconds (zero-based)

Note

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The RAR file format does not support timestamps before 1980.

RarInfo.compress_type¶

Type of compression for the archive member.

RarInfo.comment¶

Comment for the individual archive member.

RarInfo.create_system¶

System which created RAR archive.

RarInfo.extract_version¶

RAR version needed to extract archive.

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RarInfo.flag_bits¶

RAR flag bits.

RarInfo.CRC¶

CRC-32 of the uncompressed file.

RarInfo.compress_size¶

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Size of the compressed data.

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Size of the uncompressed file.

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IPBB includes ADSL2+, VDSL2, G.Fast and Ethernet technologies delivered over a hybrid of fiber optic and copper facilities which provides subscribers with significantly faster download speeds compared to traditional DSL connections. All speed tiers are asymmetrical or the download/upload speeds are different.

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