Healthcare Monitoring-based IoT framework for heart disease detection and classification

Abstract

Background: Chronic diseases like diabetes, heart disease, and cancer pose significant global health challenges, contributing to a substantial portion of worldwide mortality. Diagnosing heart disease, with its diverse signs and symptoms, remains a complex task. The growing market for connected wearable devices presents opportunities for leveraging Internet of Things (IoT) technologies in healthcare. However, diagnosing and managing heart disease effectively remains a critical concern due to its high fatality rates. Integrating IoT into the traditional healthcare system holds promise for enhancing patient outcomes, particularly for those with heart disease. Methods: A Healthcare Monitoring-based IoT framework (HM-IoT) has been developed to enable continuous monitoring of heart disease patients, eliminating the need for manual feature extraction. This framework facilitates real-time monitoring of heart failure patients through IoT-enabled devices. Data encryption using the Enhanced Encryption Standard algorithm ensures the security of patient information within the cloud platform. An Artificial Neural Network (ANN) model is employed to classify encrypted data, promptly alerting healthcare professionals to abnormal physiological conditions. Results: Evaluation in Matlab revealed impressive processing capabilities, with decryption and encryption rates of 1085 milliseconds and 1075 milliseconds, respectively. Data protection level reached 91%, while the Security Rate level attained 99%. Performance metrics, including Accuracy (98%), Sensitivity (96%), Specificity (98%), and Precision (99.4%), demonstrated the reliability of the system in detecting potential instances of heart disease. Conclusion: The Healthcare Monitoring-based IoT framework represents a significant advancement in smart healthcare solutions for heart disease management. By integrating IoT technologies with healthcare infrastructure, the framework enables real-time monitoring, enhancing prognostic capabilities and facilitating timely interventions.