Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers

Abstract

Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. The application of machine learning (ML) in healthcare enables efficient and accurate disease prediction, offering avenues for early intervention and patient support. Our study introduces an innovative diabetes prediction framework, leveraging both traditional ML techniques such as Logistic Regression, SVM, Naïve Bayes, and Random Forest and advanced ensemble methods like AdaBoost, Gradient Boosting, Extra Trees, and XGBoost. Central to our approach is the development of a novel model, DNet, a hybrid architecture combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for effective feature extraction and sequential learning. The DNet model comprises an initial convolutional block for capturing essential features, followed by a residual block with skip connections to facilitate efficient information flow. Batch Normalization and Dropout are employed for robust regularization, and an LSTM layer captures temporal dependencies within the data. Using a Kaggle-sourced real-world diabetes dataset, our model evaluation spans cross-validation accuracy, precision, recall, F1 score, and ROC-AUC. Among the models, DNet demonstrates the highest efficacy with an accuracy of 99.79% and an AUC-ROC of 99.98%, establishing its potential for superior diabetes prediction. This robust hybrid architecture showcases the value of combining CNN and LSTM layers, emphasizing its applicability in medical diagnostics and disease prediction tasks.