A Comparative Study of Machine Learning Classification Algorithms on a Public Health Dataset

Machine learning is increasingly used in healthcare for disease prediction and decision support. This paper presents a comparative study of supervised machine learning algorithms applied to a public diabetes dataset. Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest are evaluated using accuracy, precision, recall, and F1-score. The results indicate that ensemble methods such as Random Forest outperform individual classifiers. This work provides a baseline for future research and practical applications in healthcare analytics.