Severity Aware Early Diabetes Prediction Using Hybrid Machine Learning and Deep Learning Techniques

Resumen

In the recent trend and in medical domain, diabetes stands out as one of the most predominant disease and unfortunately considering as a life-threatening disease. Early prediction of diabetes plays a pivotal role in initiating prompt treatment and halting the progression of the condition. The proposed methodology not only aids in predicting the future diabetes but also finds its severity scores. By presenting this issue as a multi-class classification problem, hybrid machine learning (ML) and deep learning (DL) techniques are used to build the new hybrid model. This helps in incorporating both structural feature learning of ML and deep temporal pattern recognition of DL for better performance. The hybrid ML+DL for diabetes prediction used XGBoost, LightGBM, CatBoost ML models and Temporal Convolutional Network (TCN) as base layer, Logistic Regression (LR) as a meta-classifier.  The model is evaluated and fine-tuned for effective diabetes disease prediction with its score of severity. The experimental findings underscore the effectiveness of each component in the framework and its impact on the accuracy. The proposed work proves the sufficient amount of accuracy as 99.4%, and HML+DL compared with the recent studies in prediction of early stage of diabetes.