Prediction of Diabetes using Machine Learning Algorithms

Diabetes mellitus is one of the most prevalent chronic diseases worldwide, posing serious health risks and increasing the burden on healthcare system. Early and accurate prediction of diabetes can significantly improve patient outcomes by enabling timely medical interventions and lifestyle modifications. In this study, we developed a machine learning–based system to predict the likelihood of diabetes using clinical data. The dataset employed consisted of diagnostic attributes such as glucose level, blood pressure, insulin, body mass index (BMI), age, and other relevant parameters. To achieve reliable prediction, multiple supervised learning algorithms including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), XGBoost and Light GBM were implemented and evaluated. The models were trained and tested using the Pima Indians Diabetes Database, a widely used benchmark dataset in healthcare analytics. Performance metrics such as accuracy, precision, recall, and F1-score were used to compare the models. Among the tested algorithms, ensemble methods such as Random Forest demonstrated comparatively higher predictive performance, highlighting their ability to handle nonlinear relationships and feature interactions. This study emphasizes the importance of preprocessing techniques, including handling missing values, scaling numerical attributes, and balancing class distributions, which are critical for improving the model robustness. The results suggest that machine learning algorithms can serve as effective decision-support tools for medical practitioners when optimally optimized. This study contributes to the growing field of predictive healthcare analytics by demonstrating the potential of data-driven approaches to assist in the early diabetes diagnosis and risk assessment.