DIABETES PREDICTION MACHINE LEARNING-BASED DIABETES PREDICTION APP USING RANDOM FOREST ALGORITHM

Abstract

Diabetes is a chronic metabolic condition that causes increased blood glucose levels in millions of people worldwide. Early detection and quick action are critical for controlling this illness and preventing consequences. This work describes creating a user-friendly smartphone application for diabetes prediction using the Random Forest algorithm, a powerful machine-learning technique. The software uses user-provided information, such as age, body mass index (BMI), blood pressure, and glucose levels, to forecast the chance of acquiring diabetes. The Random Forest model was trained on a large dataset of medical records and achieved an astounding 88% accuracy on the test set. The app, created using Python and Figma, a cross-platform framework, has an intuitive and user-friendly design that allows users to enter personal information and obtain immediate forecasts. The app is a useful screening tool, allowing people to estimate their risk of getting diabetes and receive necessary medical assistance as soon as possible. The successful implementation of this diabetes prediction software highlights machine learning algorithms' potential to improve preventive healthcare and promote early intervention for chronic diseases.