IoT-enabled hybrid Slime Mould Algorithm-Support Vector Machine model for early detection of cardiovascular diseases.

Early detection is critical to improving outcomes across many diseases, yet cliniciansmust rapidly interpret heterogeneous signals, reports, and images. Automated analysis helps uncover subtle patterns and anomalies that may elude human review. This work targets real-time clinical decision support by streaming data from a portable Internet-of-Things multi sensor device to the cloud and applying a hybrid optimizer classifier pipeline for robust diagnosis. We implemented a Slime Mould Algorithm tuned Support Vector Machine (SMA-SVM) with data preprocessing and feature selection via backward elimination, then partitioned the dataset into training and test sets for evaluation. In comparative experiments, the proposed SMA-SVM outperformed established baselines including SVM, LSTM, DNN, RNN, and PSO-CNN achieving improvements of 16.11%, 16.69%, 7.79%, 11.46%, and 1.75%, respectively, for cardiovascular disease diagnosis. These results indicate that metaheuristic tuning coupled with classical margins can deliver fast, accurate, and resource-efficient predictions suitable for continuous monitoring settings.