A Comparative Study of Machine Learning Classification Algorithms on a Public Health Dataset
Abstract
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.
Fields of Study & Subjects
Artificial Intelligence in Healthcare
Internet of Things and AI
Machine Learning in Healthcare
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 2 January 2026
- Language
- EN
- Document type
- Preprint