Data-Driven Approaches to Early Diabetes Prediction: A Comprehensive Exploration of Predictive Analytics and Machine Learning

Diabetes, a prevalent chronic condition globally, necessitates early prediction for timely interventions and improved patient outcomes. Leveraging data mining techniques and advancements in medical technology, this research explores the intersection of predictive analytics and medical interventions for early diabetes detection. The literature review highlights the effectiveness of data mining methods, including SVM Classification, and Logistic Regression, in predicting diabetes and estimating its severity. The proposed methodology involves data collection, preprocessing, and classification using algorithms like Naïve Bayes, BayesNet, SVM, Logistic Regression, and k-Nearest Neighbors. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases is subjected to rigorous preprocessing to ensure accurate model training. The classification algorithms are evaluated based on accuracy, precision, recall, and F-measure, with Naïve Bayes exhibiting the highest accuracy of 73.04%. Feature selection using the ranker algorithm emphasizes the significance of each attribute, contributing to the understanding of diabetes prediction. The study underscores the potential of data-driven approaches in healthcare, particularly for conditions like diabetes, and offers insights for future research and clinical applications.