With chronic diseases on the rise, our healthcare systems are struggling to keep up, often reacting to health crises rather than preventing them. Current diagnostic methods are too slow, leading to delayed interventions and worsening patient outcomes. What if we could predict health risks such as heart disease, chronic kidney disease, breast cancer, and other diseases before they occur, simply by analyzing patient data? This project aims to use Machine Learning to identify potential health risks early, allowing healthcare providers to act before it’s too late. By enabling customized prevention strategies, personalized care, and more efficient resource allocation, this predictive system has the potential to transform our approach to healthcare—shifting from reaction to prevention and ultimately enhancing lives.
Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery In this research, the authors explore the use of Support Vector Machines (SVM), Random Forest, and Logistic Regression for classifying medical data, particularly in disease diagnosis. They conclude that while Logistic Regression is straightforward and easy to interpret, SVM and Random Forest demonstrate better performance due to their capacity to manage complex, high-dimensional data. The study highlights the benefits and challenges associated with each model and suggests that ensemble methods, such as Random Forest, may offer the best balance between accuracy and interpretability in medical applications.
The project report is availaibe at Capstone/Report/.
