Diabetes Prediction Using Machine Learning Algorithm

Diabetes is a major public health concern, demanding accurate early warning systems for efficient care. This study diagnoses diabetes using the PIMA Indian database, which includes medical predictor variables as well as lifestyle factors. Five ML algorithms support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbors—are rigorously evaluated for predictive usefulness using criteria such as accuracy, sensitivity, and specificity. The results demonstrate the Random Forest (RF) algorithm's extraordinary performance, with a staggering 98% accuracy in diabetes prediction. This study highlights the transformative potential of machine learning in illness management by providing a data-driven method for identifying at-risk patients and implementing preventative actions. The findings add significant insights to the field of diabetes prediction, emphasizing the importance of machine learning in increasing early identification and proactive healthcare measures.