An Analytical Study of Machine Learning Models for Early Diabetes Risk Prediction

Abstract

Diabetes mellitus is a chronic metabolic disorder that has become a major global health concern due to its increasing prevalence and severe long-term complications. Early prediction and diagnosis of diabetes play a crucial role in preventing disease progression and improving patient outcomes. In recent years, machine learning (ML) techniques have been widely applied to diabetes prediction using clinical and demographic data. This study presents a comprehensive analysis of existing machine learning models used for diabetic prediction. Commonly employed algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, and Artificial Neural Networks are examined based on their prediction accuracy, sensitivity, specificity, interpretability, and computational complexity. Publicly available datasets, particularly the Pima Indians Diabetes Dataset, are frequently used for model evaluation. The analysis highlights that ensemble models like Random Forest and Gradient Boosting generally outperform traditional classifiers in terms of accuracy, while simpler models such as Logistic Regression offer better interpretability for clinical decision-making. However, challenges such as data imbalance, overfitting, lack of explainability, and limited real-world clinical validation remain significant. This analytical study provides insights into the strengths and limitations of existing machine learning approaches and identifies research gaps to guide the development of more robust, interpretable, and clinically applicable diabetes prediction systems.