Synergistic Machine Learning: Enhancing Diabetes Prediction with Hybrid Deep Learning and Ensemble Models - Airlangga, Gregorius (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
Diabetes, a growing global health concern, necessitates improved predictive strategies for early and accurate detection. This study evaluates the efficacy of various machine learning and deep learning models in predicting the onset of diabetes, employing a comprehensive dataset that includes clinical and demographic variables. Traditional machine learning models such as Decision Trees, Random Forest, KNN, and XGBoost provided foundational insights, with ensemble methods showing superior performance. Furthermore, we explored the potential of deep learning by analyzing a Simple Dense Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). While these individual models yielded valuable findings, particularly in identifying true positive cases, they did not surpass the ensemble techniques in overall accuracy. The pinnacle of our research was the development of a Deep Learning Meta Learner that combined Random Forest and Gradient Boosting predictions, achieving near-perfect classification metrics, and underscoring the strength of model integration. Our findings advocate for a hybrid predictive approach that merges the nuanced feature detection of deep learning with the robust pattern recognition of ensemble models, providing an impactful direction for future diabetes prediction research. This study contributes to the advancement of medical informatics and aims to support healthcare professionals in delivering proactive and personalized patient care.