Diabetes Prediction Using Machine Learning

Abstract

Diabetes is a widespread health condition that affects millions of people around the world. If not detected early, it can lead to serious health problems. Early diagnosis is important because it helps in better treatment and prevention. Machine learning (ML) is a technology that can help predict diabetes by analyzing patient data with other health factors.

This study explores different ML models, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM) and Neural Networks to understand how they can be used for diabetes prediction. It also explains the process of preparing and cleaning data, which is necessary for improving model accuracy. Additionally, the study compares different ML models to determine which one works best. Finally, it provides suggestions for making predictions more accurate in the future, helping healthcare professionals diagnose diabetes more effectively.