Integrated Ensemble Model for Diabetes Mellitus Detection

Abstract

Diabetes Mellitus, commonly referred to as (DM), is a chronic illness that affects populations worldwide, leading to more complications such as renal failure, visual impairment, and cardiovascular disease, thus significantly compromising the individual's well-being of life. Detecting DM at an early stage is both challenging and a critical procedure for healthcare professionals, given that delayed diagnosis can result to difficulties in managing the progression of the disease. This study seeks to introduce an innovative stacking ensemble model for early DM detection, utilizing an ensemble of machine learning and deep learning models. Our proposed stacking model integrates multiple prediction learners, including Random Forest (RF), Convolutional Neural Network (CNN) with Long Short-Term Memory networks (CNN-LSTM), and Sequential Dense Layers (SDLs) as base learner models, with the Extreme Gradient Boosting model (XGBoost) serving as the Meta-Learner model. Findings demonstrate that our proposed model achieves a 99% accuracy on the Pima dataset and 97% accuracy on the DPD dataset in detecting diabetes mellitus disease. In conclusion, our model holds promise for developing a diagnostic tool for DM disease, and it is recommended to conduct further testing on the types of diabetes mellitus to enhance and evaluate its performance comprehensively.