Abstract
Biomedical IoT, predictive modeling, disease diagnosis, and temporal data analysis have become essential components in modern healthcare systems. Accurate and efficient predictive disease modeling is crucial in biomedical IoT for early detection and intervention, yet existing models like LSTM and XGBoost face limitations in handling the complexity and temporal nature of health data streams. The research article addresses this gap by introducing the Temporal Adaptive Neural Evolutionary Algorithm (TANEA), a novel approach designed to enhance the predictive capabilities in biomedical IoT systems. TANEA leverages temporal data patterns, adapts to dynamic changes in sensor readings, and optimizes feature selection through an evolutionary mechanism, resulting in a more precise and reliable predictive model. Experimental evaluations demonstrate TANEA's superior performance over traditional methods, achieving improved accuracy, reduced computational overhead, and faster convergence rates. The algorithm's adaptability to various biomedical data patterns enables more effective real-time monitoring and decision-making in IoT-based healthcare environments. These results highlight TANEA's potential to revolutionize predictive disease modeling, offering a robust solution for intelligent health monitoring and proactive healthcare interventions in the IoT ecosystem.