Advanced supervised machine learning methods for precise diabetes mellitus prediction using feature selection

Diabetes mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting millions, many of whom remain undiagnosed in the early stages. If left untreated, diabetes can result in severe complications such as blindness, stroke, cancer, joint pain, and kidney failure. Accurate and early prediction is critical for timely intervention. Recent advancements in machine learning techniques (MLT) have shown promising potential in enhancing disease prediction due to their robust pattern recognition and classification capabilities.

This study presents a comparative analysis of supervised MLT such as Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF) using the Pima Indian Diabetes dataset (PIDD) from the UCI repository. A 10-fold cross-validation approach was employed to mitigate class imbalance and ensure generalizability. Performance was evaluated using standard classification metrics: accuracy, precision, recall, and F1-score.

Among the evaluated models, SVM outperformed the others with an accuracy of 91.5%, followed by RF (90%), KNN (89%), and NB (83%). The study highlights the effectiveness of SVM in early diabetes prediction and demonstrates how model performance varies with algorithm selection.

Unlike many prior studies that focus on a single algorithm or overlook validation robustness, this research offers a comprehensive comparison of popular classifiers and emphasizes the value of cross-validation in medical prediction tasks. The proposed framework advances the field by identifying optimal models for real-world diabetes risk assessment.