Chronic Disease Prediction Using Temporal Convolutional and Attention-Guided LSTM Networks

Abstract

The early prediction of chronic illnesses such as diabetes, hypertension, and heart failure is vital for improving patient outcomes and reducing healthcare costs. However, Electronic Health Records (EHRs) present challenges due to their heterogeneous nature, containing a mix of structured (e.g., laboratory values, vital signs), unstructured (e.g., clinical notes), and temporal data (e.g., patient history). This paper proposes a novel deep ensemble learning framework that integrates Temporal Convolutional Networks (TCNs) and attention-guided Long Short-Term Memory (LSTM) networks for robust chronic disease prediction. The TCN module captures long-range temporal patterns and clinical trends across patient timelines, while the attention-enhanced LSTM module focuses on relevant time-steps and feature segments critical for diagnosis. The ensemble approach combines these deep representations to improve classification accuracy, reduce false positives, and provide resilience against data irregularities. Experiments conducted on benchmark multi-modal EHR datasets demonstrate the model’s superiority over traditional RNN, LSTM, and CNN-based methods. The proposed architecture not only enhances prediction performance but also contributes toward explainable and proactive healthcare decision-making.