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

J. Shafiq Mansoor ¹, Kamalraj Subramaniam ²

The review discused the Healthcare Monitoring based IoT framework (HM-IoT) and determined the security rate and classification accuracy.

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.

Keywords: Healthcare Monitoring, Internet of Things, Artificial Neural Networks, Security, Enhanced Encryption Standard algorithm

References

Adeniyi, E.A., Ogundokun, R.O., & Awotunde, J.B. (2021). IoMT-based wearable body sensors network healthcare monitoring system. IoT in healthcare and ambient assisted living, 103-121.

Alam, A. (2022). Cloud-based e-learning: scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. In Computer Communication, Networking and IoT: Proceedings of 5th ICICC 2021, Volume 2. Singapore: Springer Nature Singapore, 1-9.

Ali, M.M., Paul, B.K., Ahmed, K., Bui, F.M., Quinn, J.M., & Moni, M.A. (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136, 104672.

Alraja, M.N., Barhamgi, H., Rattrout, A., & Barhamgi, M. (2021). An integrated framework for privacy protection in IoT—Applied to smart healthcare. Computers & Electrical Engineering, 91, 107060. https://doi.org/10.1016/j.compeleceng.2021.107060

Dami, S., & Yahaghizadeh, M. (2021). Predicting cardiovascular events with deep learning approach in the context of the internet of things. Neural Computing and Applications, 33, 7979-7996.

Domínguez-Gil, B., Ascher, N., Capron, A.M., Gardiner, D., Manara, A.R., Bernat, J.L., & Delmonico, F.L. (2021). Expanding controlled donation after the circulatory determination of death: statement from an international collaborative. Intensive care medicine, 47, 265-281.

Elayan, H., Aloqaily, M., & Guizani, M. (2021). Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet of Things Journal, 8(23), 16749-16757.

Fanara, S., Aprile, M., Iacono, S., Schirò, G., Bianchi, A., Brighina, F., & Salemi, G. (2021). The role of nutritional lifestyle and physical activity in multiple sclerosis pathogenesis and management: a narrative review. Nutrients, 13(11), 3774. https://doi.org/10.3390/nu13113774

Ganesan, M., & Sivakumar, N. (2019). IoT based heart disease prediction and diagnosis model for healthcare using machine learning models. In IEEE international conference on system, computation, automation and networking (ICSCAN), 1-5.

Haleem, A., Javaid, M., Singh, R.P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors international, 2, 100117. https://doi.org/10.1016/j.sintl.2021.100117

Haque, M.S.M., Hassan, M.R., & Hossain, M.K. (2021). Underlying Concepts and Understandings of Internet of Things (IoT). Manchester Journal of Artificial Intelligence and Applied Sciences, 2(2), 101-110.

Hartmann, M., Hashmi, U. S., & Imran, A. (2022). Edge computing in smart health care systems: Review, challenges, and research directions. Transactions on Emerging Telecommunications Technologies, 33(3), e3710. https://doi.org/10.1002/ett.3710

https://doi.org/10.1016/j.compbiomed.2021.104672

https://www.kaggle.com/code/cdabakoglu/heart-disease-classifications-machine-learning

Hu, J., Liang, W., Hosam, O., Hsieh, M. Y., & Su, X. (2022). 5GSS: A framework for 5G-secure-smart healthcare monitoring. Connection Science, 34(1), 139-161.

Ihnaini, B., Khan, M.A., Khan, T.A., Abbas, S., Daoud, M.S., Ahmad, M., & Khan, M.A. (2021). A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/4243700

Jansi Rani, S.V., Chandran, K.S., Ranganathan, A., Chandrasekharan, M., Janani, B., & Deepsheka, G. (2022). Smart wearable model for predicting heart disease using machine learning: Wearable to predict heart risk. Journal of Ambient Intelligence and Humanized Computing, 13(9), 4321-4332.

Khowaja, S.A., Khuwaja, P., Dev, K., & D’Aniello, G. (2023). VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors. Neural Computing and Applications, 35(22), 16175-16192.

Kumar, Y., Koul, A., Sisodia, P. S., Shafi, J., Kavita, V., Gheisari, M., & Davoodi, M.B. (2021). Heart failure detection using quantum-enhanced machine learning and traditional machine learning techniques for internet of artificially intelligent medical things. Wireless Communications and Mobile Computing, 2021, 1-16.

Malik, P.K., Sharma, R., Singh, R., Gehlot, A., Satapathy, S.C., Alnumay, W.S., & Nayak, J. (2021). Industrial Internet of Things and its applications in industry 4.0: State of the art. Computer Communications, 166, 125-139.

Marques, J.A.L., Gois, F.N.B., Da Silveira, J.A.N., Li, T., & Fong, S.J. (2022). AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions. In Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data. Academic Press, 101-121.

Mbunge, E., & Muchemwa, B. (2022). Towards emotive sensory Web in virtual health care: Trends, technologies, challenges and ethical issues. Sensors International, 3, 100134. https://doi.org/10.1016/j.sintl.2021.100134

Mehmood, A., Iqbal, M., Mehmood, Z., Irtaza, A., Nawaz, M., Nazir, T., & Masood, M. (2021). Prediction of heart disease using deep convolutional neural networks. Arabian Journal for Science and Engineering, 46(4), 3409-3422.

Mishra, S., Thakkar, H.K., Mallick, P.K., Tiwari, P., & Alamri, A. (2021). A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection. Sustainable Cities and Society, 72, 103079. https://doi.org/10.1016/j.scs.2021.103079

Pai, M.M., Ganiga, R., Pai, R.M., & Sinha, R.K. (2021). Standard electronic health record (EHR) framework for Indian healthcare system. Health Services and Outcomes Research Methodology, 21(3), 339-362.

Patro, S.P., Padhy, N., & Chiranjevi, D. (2021). Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning. Evolutionary intelligence, 14(2), 941-969.

Rani, P., Kumar, R., Ahmed, N.M.S., & Jain, A. (2021). A decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments, 7(3), 263-275.

Ru, L., Zhang, B., Duan, J., Ru, G., Sharma, A., Dhiman, G., & Masud, M. (2021). A detailed research on human health monitoring system based on internet of things. Wireless Communications and Mobile Computing, 2021, 1-9.

Sekar, J., Aruchamy, P., Sulaima Lebbe Abdul, H., Mohammed, A.S., & Khamuruddeen, S. (2022). An efficient clinical support system for heart disease prediction using TANFIS classifier. Computational Intelligence, 38(2), 610-640.

Sinha, A., & Singh, S. (2021). Detailed analysis of medical IoT using wireless body sensor network and application of IoT in healthcare. Human Communication Technology: Internet of Robotic Things and Ubiquitous Computing, 401-434.

Soni, M., & Singh, D.K. (2022). LAKA: lightweight authentication and key agreement protocol for internet of things based wireless body area network. Wireless Personal Communications, 127(2), 1067-1084.

Verma, P., Sood, S.K., & Kalra, S. (2018). Cloud-centric IoT based student healthcare monitoring framework. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1293-1309.

Vishwanadham Mandala, Srinivas Naveen Dolu Surabhi, V. R. Balaji et al., (2024). Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning, journal of Angiotherapy, 8(3), 1-8, 9535

Zhenya, Q., & Zhang, Z. (2021). A hybrid cost-sensitive ensemble for heart disease prediction. BMC medical informatics and decision making, 21, 1-18.

Zhu, X. (2021). Complex event detection for commodity distribution Internet of Things model incorporating radio frequency identification and Wireless Sensor Network. Future Generation Computer Systems, 125, 100-111.