Diabetes Prediction Using Machine Learning Techniques: A Comprehensive Analysis

Abstract

This article analyzes machine learning methods for predicting diabetes using clinical data. The study focuses on four models: Logistic Regression, Decision Tree, Gradient Boosting, and XGBoost. Patient data, including age, gender, blood glucose levels, and body mass index, were used to evaluate the accuracy of these models. Data preprocessing techniques such as scaling and normalization were applied to improve results. The findings reveal that ensemble methods like Gradient Boosting and XGBoost outperform traditional models in prediction accuracy. These machine learning approaches not only enhance prediction but also identify key risk factors, aiding in early diagnosis and timely prevention of diabetes.

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