RSO-MRSA: rat swarm optimization based modified Rivest–Shamir–Adleman for secure and efficient healthcare monitoring system

Heart disease is the main reason behind the increased mortality rate worldwide, identifying and diagnosing heart disease with disparate features is a highly complicated task. The rapid development of Internet of Things applications provides greater chances to deliver enhanced clinical results with minimized error. However, the survival percentage is considerably less for the people who experienced sudden heart attacks. There it demands an effective healthcare monitoring system to monitor patient health and predict heart failures accurately. Numerous healthcare monitoring systems for monitoring and managing patient health conditions already exist, but they failed to provide data security. To address this issue, this paper proposes an effective health monitoring system with the integration of security modules and disease prediction modules. The security module uses a modified Rivest–Shamir–Adleman algorithm to transmit data securely to the healthcare monitoring system. The prediction module uses a novel modified AdaBoost bidirectional-based rat swarm (MAB-RS) classifier to accurately predict and classify normal (ie. healthy) and abnormal (ie. patient with heart disease) cases. After prediction, the health monitoring system generates an alert automatically and notifies the doctor about the abnormal condition of the patient. Some of the measures namely accuracy, precision, recall, specificity, f1-score, encryption, and decryption time are measured to analyze the performance of the proposed MAB-RS system. The experimental result shows that the proposed system attains a greater accuracy rate of about 98.3% than other compared techniques.