An IoT-Driven Framework for Healthcare Prediction using Loss-Attention Physics-Informed Neural Networks and Artificial Protozoa Optimization

In recent years, the healthcare industry now faces both new opportunities and difficulties because of what the Internet of Things has explosive growth, particularly in predictive diagnostics and real-time patient monitoring. This has led to the development of intelligent frameworks aimed at enhancing accuracy, efficiency, and responsiveness in healthcare prediction systems. This research proposes a novel approach An IoT-Driven Framework for Healthcare Prediction Applying Cognitive Networks Directed by Loss-Attention Theory and Artificial Protozoa Optimization. Unlike conventional hybrid AI frameworks, the proposed integration of LA-PINN and APO introduces a dual-layer optimization mechanism that merges physics-informed learning with adaptive bio-inspired search. To address data heterogeneity and missing values, the data is preprocessed utilizing Hierarchical Graph Collaborative Filtering (HGCF) in three different ways, this combines and polishes the data. After the dataset has been improved, LA-PINN, or It is investigated using Loss-Attention Physics-Informed Neural Networks. This dynamically adjusts the importance of data points through attentional mechanisms, enhancing model robustness and accuracy. This combination enables the model to simultaneously maintain interpretability from physical constraints and achieve faster, noise-resilient convergence, marking a distinct advancement over existing healthcare optimization strategies. Python is used to implement the system, and measures including computing efficiency, precision, and forecast precision are employed to evaluate its efficacy. The proposed LA-PINN-APO method the accuracy is 99%, Recall is 97%, highest specificity of 98% is better than other exiting methods.