Optimizing Diabetes Prediction with Machine Learning: Model Comparisons and Insights

Abstract

This study aims to predict diabetes using various machine learning models and compare their performances. The dataset utilized contains health indicators and lifestyle factors from a diverse population. The models evaluated include Random Forest, Logistic Regression, Support Vector Machine (SVM), and Gradient Boosting. Results indicate that Gradient Boosting outperforms other models in terms of accuracy, precision, and recall, making it a robust choice for diabetes prediction. The analysis provides insights into the most significant features contributing to diabetes prediction and highlights the potential of machine learning in medical diagnosis.