Analysis of Different Machine Learning Models for Diabetes Prediction

This paper presents a comparative analysis of various machine learning models applied to a diabetes dataset. The study evaluates a range of ML algorithms, employing exploratory data analysis and feature engineering techniques. The primary objective is to forecast the onset of diabetes mellitus in a high-risk population of Pima Indians. Each model is trained, evaluated using performance metrics such as accuracy and F1- score, and assessed using ROC curves and AUC values. The results highlight the performance and suitability of each model for the prediction task. Notably, an Support Vector Machines (SVM) model achieved an accuracy of 77.27%. This study contributes valuable insights for selecting appropriate models in similar scenarios.