Diabetes Prediction Using Machine Learning

Diabetes mellitus is a persistent metabolic condition impacting millions globally. Preventing problems requires early detection. This study uses a clinical cohort from Medical Centre Chittagong, Bangladesh, and the Pima Indian Diabetes dataset to create machine learning-based classification models for diabetes prediction. Five supervised algorithms, including k-nearest neighbours, naïve Bayes, support vector machines, decision trees, and multilayer perceptron’s, were trained and validated using ten-fold cross-validation following thorough data pre-processing and the selection of nine essential features. Performance measurements encompassed accuracy, precision, recall, F-measure, and area under the ROC curve. Model accuracies varied between 81.1% and 97.6%, whereas ensemble techniques had a dependability of up to 98.7% and an AUC of 0.95. These results show how integrated machine learning pipelines can help clinicians make clinical decisions when it comes to diabetes risk screening.