#DataScientist

[d0nutzgg]

Predicting Alzheimer's With Machine Learning

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.

Model Creation

The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).

MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.

CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.

SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.

nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.

After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.

Model Performance

To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.

The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.

The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.

The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.

However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.

Conclusion

The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.

Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.

[generativetenebrousautomata]

[thalia-pages]

pin

[courseswebs]

Top Data Science Courses With Certificate ⬇️

1-IBM Data Science Professional Certificate

https://imp.i384100.net/YgYndj

2-Google Data Analytics Professional Certificate

https://imp.i384100.net/x9jAxk

3-Google Data Analytics Professional Certificate

https://imp.i384100.net/x9jAxk

4-Introduction to Data Science Specialization

https://imp.i384100.net/Ryqyry

5-Applied Data Science with Python Specialization

https://imp.i384100.net/GjkEPn

6-Google Advanced Data Analytics Professional Certificate

https://imp.i384100.net/1r5E3B

7-What is Data Science?

https://imp.i384100.net/JzmRaN

8-Data Science Specialization

https://imp.i384100.net/BX9BmB

9-Python for Data Science, AI & Development

https://imp.i384100.net/g1ARWv

10-Foundations of Data Science

https://imp.i384100.net/nL2Wza

11-IBM Data Analyst Professional Certificate

https://imp.i384100.net/jWGKxa

12-Machine Learning Specialization

https://imp.i384100.net/k0gLAV

[openprogrammer]

It happens 💬 😂😂😂😂😂😂😂😂 Follow @openprogrammer Data +Science +ML +Python / R+Deep Learning + Neutral Network + AI #data #science #datascienceforindia #datascientist #datascience #machinelearning #ml #python #sql #ai #artificialintelligenceai #deeplearning #neuralnetworks #maths #bigdata #techno #technology #dataanalysis #hadoop #java #coderforlife #codinglife #coding#ruby #javascript #scala #perl #go #swift #java https://www.instagram.com/p/Cnlygopv-4l/?igshid=NGJjMDIxMWI=

[thebattlefordatasupremacy]

Data Engineer vs. Data Scientist The Battle for Data Supremacy

In the rapidly evolving landscape of technology, two professions have emerged as the architects of the data-driven world: Data Engineers and Data Scientists. In this comparative study, we will dive deep into the worlds of these two roles, exploring their unique responsibilities, salary prospects, and essential skills that make them indispensable in the realm of Big Data and Artificial Intelligence.

The world of data is boundless, and the roles of Data Engineers and Data Scientists are indispensable in harnessing its true potential. Whether you are a visionary Data Engineer or a curious Data Scientist, your journey into the realm of Big Data and AI is filled with infinite possibilities. Enroll in the School of Core AI’s Data Science course to day and embrace the future of technology with open arms.

[womaneng]

Why NumPy?

So in data science what is the thing we deal with?

Data, right?

Yes, and how do we store the data?

Array? List? Dictionary?

Right.. Now think here we’re dealing with tons and tons of data and the data where we’re storing is super slow. What is gonna be the experience?

Terrible.

Absolutely, and that is why we use NumPy. In data science NumPy is the fundamental package to perform high-level mathematical computations on multi-dimensional arrays. So we store the data in NumPy arrays, and use NumPy to perform those computations. . . .

[datasculptor]

Text-to-shape generation models are a significant enabler for new innovative tools in creative design and manufacturing and animation and gaming in practice.

[techasoft-pvt-ltd]

✔️ AI And ML Trends That Will Transform The Way People Work And Live ✔️

Artificial Intelligence (AI) and Machine Learning (ML) have already started to revolutionize the way people work and live, and the next decade will witness even more transformative advancements. Here are some AI and ML trends that will reshape various aspects of our lives. Read Here 🌐 - https://bit.ly/3qgKndw

[kochivamarketing]

Become Coding Expert with Data Structure Training Institute

Are you looking to take your programming skills to the next level?

Master the foundations of computer science with our comprehensive data structures and algorithms training!

Why Data Structures Matter: Data structures form the backbone of efficient and organized programming. Understanding how to manipulate and utilize data is crucial for solving complex problems and creating optimized algorithms. Whether you're a beginner or an experienced coder, diving deep into data structures is a game-changer for your coding journey.

At Kochiva, the premier Data Structure Training Institute in India, we provide in-depth training on essential data structures like arrays, linked lists, stacks, queues, trees, graphs, and more. Our expert instructors with years of industry experience will help you truly understand how these data structures work and how to implement them efficiently in any programming language. You’ll gain hands-on experience with coding challenges and projects designed to hone your skills.

Don't just learn data structures; become a coding maestro! 🎓 Enroll in Kochiva now and sculpt your coding prowess with the best in the industry.

Your coding adventure awaits! 🚀💻

[excelhelps]

BRING DATA FROM THE WEB INTO EXCEL!

[techblog-365]

WHAT IS THE PURPOSE OF DATA SCIENCE?

Data Science's main aim is to identify trends inside data. In order to Analyze and draw lessons from the results, it utilizes different statistical techniques. A Data Scientist should carefully scrutinize the information from data acquisition, wrangling and pre-processing. Then, from the details, he has the duty to make predictions. To read more visit: https://www.rangtech.com/blog/data-science/what-is-the-purpose-of-data-science