IoMT with Cloud-Based Disease Diagnosis Healthcare Framework for Heart Disease Prediction Using Simulated Annealing with SVM

Internet of Medical Things (IoMT) interlinks a collection of intelligent sensors on the patient’s body to observe and interpret multimodal health data, including the patient’s physiological and psychological signals. The large amount of data produced by IoMT devices in medical application is examined on cloud by replacing the restricted memory as well as processing resources of handheld tools. In this study, an IoMT-based healthcare diagnosis model is introduced by the use of intelligent techniques. This paper proposes a new IoMT-based disease diagnosis healthcare framework for heart disease prediction using the BBO-SVM model. The proposed model involves the parameter tuning of SVM using the BBO algorithm. The validation of the proposed model takes place using a Statlog Heart disease dataset. The detailed experimental analysis strongly pointed out that the proposed BBO-SVM model has shown excellent results by attaining a maximum precision of 88.33%, recall of 87.60%, accuracy of 89.26%, F-score of 87.96%, and kappa value of 78.27%.