Early Diabetes Prediction Through Multiple Machine Learning Models: A Comparative Analysis

People of every stage of life are affected by diabetes, which has emerged as a significant health issue in modern society. A total of 463 million individuals between the ages of 20 and 79 were estimated to have diabetes in 2019, and projections suggest that figure might rise to 700 million by 2045. Healthcare systems face serious difficulties as a result of this spike, which raises the need for medical treatment, increases disability rates, and causes more early deaths. Diabetes is linked to a number of health problems, including cardiovascular disorders, renal problems, and eyesight loss. Early identification and treatment, however, can lessen or delay these issues. Four ML models are used in this work to categorize and forecast diabetes: DT, XGBoost, LR, and RF. The results indicate that the XGBoost model outperforms the others, achieving an accuracy rate of 96.04%.