A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

-- In many nations, diabetes is becoming a significant health problem, and early identi - fication and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Dia - betes dataset, this study attempts to evaluate the efficacy of several machine - learning methods for diabetes prediction. The collection includes infor - mation on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k - Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool. Diabetes is a chronic metabolic disease af - fecting millions worldwide and is a significant cause of morbidity and death [1]. High blood glucose levels characterize the disorder and can result in some complications, including cardiovascular disease, stroke, blindness, and amputations. To prevent or postpone com - plications, diabetes must be recognized and treated as soon as feasible; however, this can be challenging because symptoms may be mild or absent [2]. Machine learning (ML) is a subfield of artificial intelligence that comprises the de - velopment of algorithms that can learn from data and generate inferences or predictions without being explicitly programmed. ML algorithms are beneficial in several fields, in - cluding healthcare.