Adaptive Deep Learning Framework for Early Multi-Disease Risk Prediction in Healthcare Systems

Abstract

Early prediction of multiple chronic diseases is essential for improving patient outcomes and reducing healthcare costs. This paper presents an Adaptive Deep Learning Framework for Early Multi-Disease Risk Prediction in Healthcare Systems using advanced machine learning techniques. The proposed system integrates heterogeneous healthcare data, including clinical records, laboratory results, and patient history, to build a unified predictive model. Deep learning architectures such as hybrid CNN-LSTM networks are employed to capture both spatial and temporal patterns in patient data. The framework incorporates feature selection and optimization techniques to enhance model efficiency and accuracy. Additionally, adaptive learning mechanisms are introduced to update the model dynamically as new patient data becomes available. Experimental results demonstrate that the proposed system achieves high accuracy and generalization across multiple disease prediction tasks. Integration with prior research in ensemble learning and healthcare analytics further strengthens system robustness. The study highlights the potential of adaptive deep learning models in enabling proactive, data-driven healthcare systems capable of early disease detection and improved clinical decision-making.