Key result
IoT-enabled hybrid SMA-SVM model outperforms baseline models with ~98% accuracy in detecting cardiovascular disease.
Why the study?
Clinicians must rapidly interpret heterogeneous signals, reports, and images, creating a need for automated analysis to detect subtle patterns and anomalies in cardiovascular diseases.
Does a hybrid Slime Mould Algorithm-Support Vector Machine (SMA-SVM) model improve diagnostic accuracy for cardiovascular diseases compared to standard machine learning algorithms?
Does a hybrid Slime Mould Algorithm-Support Vector Machine (SMA-SVM) model improve diagnostic accuracy for cardiovascular diseases compared to standard machine learning algorithms?
Absolute Event Rate: 97.77% vs 96.08%
p-value: p=<0.05
A hybrid SMA-SVM machine learning model demonstrates high accuracy and sensitivity for cardiovascular disease detection, offering a potential tool for IoT-based continuous clinical monitoring.
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.
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Venkatesan et al. (2026) studied Cardiovascular disease (n=603). IoT-enabled hybrid Slime Mould Algorithm-Support Vector Machine (SMA-SVM) model vs. SVM, DNN, RNN, LSTM, and PSO-CNN models was evaluated on Classification accuracy (p=<0.05). The IoT-enabled hybrid SMA-SVM model achieved 97.77% accuracy in detecting cardiovascular disease, outperforming PSO-CNN and other baseline models.
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