Diabetes / Heart Disease Prediction Using Machine Learning

Diabetes and heart disease are important health conditions for which assessment of risk factors may support timely discussionwith healthcare professionals. This paper proposes a machine-learning-based prediction system that uses selected attributes froman appropriate, documented dataset to classify records into dataset-defined categories for diabetes or heart disease. Possiblealgorithms include Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine. The workflow includes datacleaning, preprocessing, model training, testing, and presentation through a simple interface. The system is intended foreducational and research purposes only. Its output is not a diagnosis, does not replace clinical assessment, and must not be used tomake treatment decisions. This is a proposed design and evaluation plan; no measured accuracy or clinical effectiveness isclaimed