Self-Adaptive Deep Learning Framework for Real-Time Disease Prediction Using Streaming Healthcare Data

Abstract

The rapid generation of real-time healthcare data from wearable devices and clinical monitoring systems has created new opportunities for continuous disease prediction. This paper presents a Self-Adaptive Deep Learning Framework for Real-Time Disease Prediction using streaming healthcare data. The proposed system processes continuous data streams such as heart rate, activity levels, and physiological signals to enable early detection of potential health risks. A hybrid deep learning architecture combining Convolutional Neural Networks and Long Short-Term Memory networks is employed to capture both spatial and temporal patterns in streaming data. The framework incorporates self-adaptive learning mechanisms that dynamically update model parameters based on incoming data, ensuring robustness against concept drift. Additionally, lightweight edge computing modules are integrated to support low-latency predictions. Experimental results demonstrate improved accuracy and responsiveness compared to traditional batch learning models. Integration with prior research in healthcare analytics enhances system scalability and reliability. The study highlights the potential of real-time adaptive AI systems in enabling proactive and continuous healthcare monitoring for improved patient outcomes.