Reforming disease prognosis and treatment prediction for palliative care with hybrid metaheuristic deep neural architectures in IoT healthcare ecosystems

The increasing integration of the Internet of Things (IoT) in healthcare has led to massive, high-dimensional data streams, demanding advanced, adaptive learning models for timely and accurate clinical predictions, especially in sensitive domains such as palliative care. Existing models often suffer from high computational overhead, sensitivity to learning rate variations, and difficulties in handling non-stationary data, which impedes their ability to deliver accurate and prompt predictions in high-risk medical scenarios. To address these limitations, this study proposes a Hybrid Metaheuristic-Driven Deep Neural Architectures (HMDNA) that combines a Deep Neural Network (DNN) with Cuckoo Search Optimization (CSO) for sepsis detection and prognosis. The methodology follows a structured pipeline encompassing data preprocessing, model training, and optimization. Time-series ICU data were preprocessed using k-NN imputation and min-max scaling, followed by DNN training with CSO-based optimization applied at initialization, mid-training, and fine-tuning stages. Implemented using TensorFlow and trained on an NVIDIA Tesla V100 GPU, the model achieved an accuracy of 92.7%, precision of 91.8%, recall of 90.3%, and F1 score of 91.4%. These results significantly outperform baseline models including traditional DNN (85.3% accuracy), DNN + GA (88.5%), DNN + PSO (89.2%), and DNN + ACO (90.1%). The proposed model demonstrated faster convergence, better generalization, and robustness to real-time variability in healthcare data. By combining the strengths of deep learning and metaheuristic optimization, this approach ensures reliable performance in dynamic and unpredictable clinical environments. The study highlights the potential of adaptive, hybrid AI models in enhancing healthcare decision-making, particularly in critical care scenarios where prediction accuracy and model responsiveness are vital for improving patient outcomes.