Predicting Co-Morbid Chronic Diseases: Hypertension and Diabetes Using Deep Learning

This research proposes a deep learning-oriented method for forecasting the co-morbidity of hypertension and diabetes utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The main contribution of this research is the creation of a hybrid deep learning framework that proficiently merges CNNs for feature extraction and RNNs for sequence modeling, facilitating precise disease prediction from electronic health records (EHR). The model is trained using a vast dataset that includes demographic, clinical, and lifestyle factors to improve predictive accuracy. Our experimental findings indicate that the proposed model attains 92.5% accuracy, 90.1% precision, and 88.7% recall, surpassing traditional machine learning techniques. Additionally, we incorporate SHAP (Shapley Additive Explanations) values to enhance model interpretability, enabling healthcare professionals to pinpoint essential risk factors that affect predictions. The results underscore the promise of deep learning in fostering early disease detection, streamlining patient care, and lowering healthcare expenses. This research establishes a base for prospective AI-driven predictive analytics in healthcare, with potential for real-world application and further advancement through federated learning and real-time clinical data integration.