Diabetes Early Prediction Using Machine Learning and Ensemble Methods

Abstract

This study aims to develop and validate an enhanced early prediction model for diabetes utilizing machine learning and ensemble techniques, aimed at addressing the rapid increase in diabetes prevalence and the associated healthcare burden. Leveraging diverse datasets, including the Pima Indian Diabetes Dataset, electronic health records from local hospitals, and wearable device data, this research employs a variety of innovative methods. Generative Adversarial Networks (GAN) are used for data augmentation to address class imbalances, while SHAP (Shapley Additive exPlanations) provides interpretability for machine learning predictions, enhancing trust and understanding in clinical applications. The methodology integrates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks—comparing their efficacy in diabetes prediction. Ensemble methods further refine the predictive accuracy, reliability, and applicability of the models. The study evaluates these models based on standard performance metrics such as accuracy, precision, recall, and F1-score across different configurations and combined approaches. Results indicate that ensemble methods significantly enhance predictive performance, achieving higher accuracy and precision compared to individual models. Particularly, the integration of deep learning techniques with traditional machine learning models provides substantial improvements in detecting early signs of Type 1 and Type 2 diabetes, utilizing insights from insulin and C-peptide data. The application of XAI techniques like SHAP not only clarifies model decisions but also assists in tailoring interventions and management strategies in clinical setting.