2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 4 , Issue 2 , PP: 39-48, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Zaid Abualkishik 1 * , Ali A. Alwan 2
Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.
Sustainability, Healthcare system, Clustering, Deep learning, Disease diagnosis, CH Selection
[1] Sood, S.K. and Mahajan, I., 2017. Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus. Computers in Industry, 91, pp.33-44.
[2] Sood, S.K., Sood, V., Mahajan, I. and Sahil, S., 2020. Fog–cloud assisted IoT-based hierarchical approach for controlling dengue infection. The Computer Journal.
[3] Said, O. and Tolba, A., 2021. Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture. Applied Sciences, 11(8), p.3623.
[4] Verma, P. and Sood, S.K., 2018. Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, pp.27-38.
[5] Sharma, M., Singh, G. and Singh, R., 2019. An advanced conceptual diagnostic healthcare framework for diabetes and cardiovascular disorders. arXiv preprint arXiv:1901.10530.
[6] Jiang, L., Chen, L., Giannetsos, T., Luo, B., Liang, K. and Han, J., 2019. Toward practical privacy-preserving processing over encrypted data in IoT: an assistive healthcare use case. IEEE Internet of Things Journal, 6(6), pp.10177-10190.
[7] Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P.M., Sundarasekar, R. and Hsu, C.H., 2018. Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless personal communications, 102(3), pp.2099-2116.
[8] Daejin Park, S.L., A Real-Time Abnormal Beat Detection Method Using a Template Cluster for the ECG Diagnosis of IoT Devices.
[9] Jebaseeli, T.J., Durai, C.A.D. and Peter, J.D., 2020. IOT based sustainable diabetic retinopathy diagnosis system. Sustainable Computing: Informatics and Systems, 28, p.100272.
[10] Kumar, P.M., Lokesh, S., Varatharajan, R., Babu, G.C. and Parthasarathy, P., 2018. Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems, 86, pp.527-534.
[11] Balakrishna, S., Thirumaran, M. and Solanki, V.K., 2020. IoT sensor data integration in healthcare using semantics and machine learning approaches. In A Handbook of Internet of Things in Biomedical and Cyber Physical System (pp. 275-300). Springer, Cham.
[12] Alshammari, H., Abd El-Ghany, S. and Shehab, A., 2020. Big IoT healthcare data analytics framework based on fog and cloud computing. Journal of Information Processing Systems, 16(6), pp.1238-1249.
[13] Kaur, P., Kumar, R. and Kumar, M., 2019. A healthcare monitoring system using random forest and internet of things (IoT). Multimedia Tools and Applications, 78(14), pp.19905-19916.
[14] Ganesan, M. and Sivakumar, N., 2019, March. IoT based heart disease prediction and diagnosis model for healthcare using machine learning models. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE.
[15] Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A.O., Asghari, P., Souri, A., Mazaherinezhad, A., Bohlouli, M. and Rawassizadeh, R., 2021. A diagnostic prediction model for chronic kidney disease in internet of things platform. Multimedia Tools and Applications, 80(11), pp.16933-16950.
[16] Bharathi, R., Abirami, T., Dhanasekaran, S., Gupta, D., Khanna, A., Elhoseny, M. and Shankar, K., 2020. Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems. Sustainable Computing: Informatics and Systems, 28, p.100453.
[17] Akhbarifar, S., Javadi, H.H.S., Rahmani, A.M. and Hosseinzadeh, M., 2020. A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment. Personal and Ubiquitous Computing, pp.1-17.
[18] Nguyen, P.T., Huynh, V.D.B., Vo, K.D., Phan, P.T., Elhoseny, M. and Le, D.N., 2021. Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data. CMC-COMPUTERS MATERIALS & CONTINUA, 66(3), pp.2555-2571.
[19] Praveen et al., 2021. Oppositional Glowworm Swarm Optimization Based Clustering With Deep Neural Network Classification for Clinical Decision Support Systems, Computers, Materials & Continua DOI:10.32604/cmc.2020.012398
