Diabetes Prediction Using Classification Algorithms

Abstract: Diabetes is a metabolic disorder characterized by elevated blood glucose levels, leading to potential complications affecting various organs. This study aims to employ machine learning algorithms to accurately predict diabetes at an early stage. A dataset from Kuppam, AP, was utilized to analyze the relationship between chronic diseases and diabetes. Machine learning models, including K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and logistic regression were implemented. Our findings demonstrate that Decision Tree outperforms other models in predicting diabetes, suggesting its potential as a valuable tool for early detection and intervention