Estimating the Effectiveness of Machine Learning methods for Patients Health Care Monitoring in Remote Location
Keywords:
Machine Learning, Health Monitoring, Remote Area, Elderly PeopleAbstract
Ambient Assistive Living has gained attention from academics because of the issues with an older population and the problems resulting in social and health care. Managing or even lowering healthcare expenditures while enhancing service quality is a top priority for the authorities. Using suitable domain knowledge is required to develop, implement, and validate any solution, even though technologies have a significant role in realizing these goals. To overcome these obstacles, remote real-time surveillance of a person's wellness can be utilized to spot relapsing problems and allow for early diagnosis. Therefore, the study discussed in this research aims to create a smart healthcare tracking scheme to watch over old individuals from a distance. A Machine Learning based Health Monitoring System (ML-HMS) is designed in this article. The technology discussed in this article concentrates on the capability to monitor a person's physiological information to identify particular illnesses that can help with early intervention techniques. Support Vector Machine (SVM) is accomplished by correctly processing and analysing the sensory data obtained while communicating the discovery of a condition to the proper professional. The conclusion shows that the suggested approach can enhance clinical decision assistance while promoting Early Intervention Activities. The thorough simulation findings show that the suggested system performs better than expected, with reduced latency and packet loss. As a result, the scheme handles data modification and gathering effectively and affordably.
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Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43.
Nikou, S., Agahari, W., Keijzer-Broers, W., & de Reuver, M. (2020). Digital healthcare technology adoption by older adults: A capability approach model. Telematics and Informatics, 53, 101315.
Llorente-Barroso, C., Kolotouchkina, O., & Mañas-Viniegra, L. (2021). The enabling role of ICT to mitigate the negative effects of emotional and social loneliness of the elderly during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(8), 3923.
Huifeng, W., Kadry, S. N., & Raj, E. D. (2020). Continuous health monitoring of sportspersons using IoT devices-based wearable technology. Computer Communications, 160, 588-595.
Strain, T., Wijndaele, K., & Brage, S. (2019). Physical activity surveillance through smartphone apps and wearable trackers: examining the UK potential for nationally representative sampling. JMIR mHealth and uHealth, 7(1), e11898.
Ghayvat, H., Pandya, S., Bhattacharya, P., Zuhair, M., Rashid, M., Hakak, S., & Dev, K. (2021). CP-BDHCA: Blockchain-based Confidentiality-Privacy preserving Big Data scheme for healthcare clouds and applications. IEEE Journal of Biomedical and Health Informatics, 26(5), 1937-1948.
Selvaraj, S., & Sundaravaradhan, S. (2020). Challenges and opportunities in IoT healthcare systems: a systematic review. SN Applied Sciences, 2(1), 1-8.
Nasiri, S., & Khosravani, M. R. (2020). Progress and challenges in the fabrication of wearable sensors for health monitoring. Sensors and Actuators A: Physical, 312, 112105.
Islam, M., & Rahaman, A. (2020). Development of smart healthcare monitoring system in IoT environment. SN computer science, 1(3), 1-11.
Peng, B., Zhao, F., Ping, J., & Ying, Y. (2020). Recent advances in nanomaterial‐enabled wearable sensors: material synthesis, sensor design, and personal health monitoring. Small, 16(44), 2002681.
Islam, M., & Rahaman, A. (2020). Development of smart healthcare monitoring system in IoT environment. SN computer science, 1(3), 1-11.
Butt, S. A., Jamal, T., Azad, M. A., Ali, A., & Safa, N. S. (2022). A secure multivariant framework for smart mobile health applications. Transactions on Emerging Telecommunications Technologies, 33(8), e3684.
Kavitha, D., & Ravikumar, S. (2021). IoT and context‐aware learning‐based optimal neural network model for real‐time health monitoring—transactions on Emerging Telecommunications Technologies, 32(1), e4132.
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on deep ensemble learning and feature fusion. Information Fusion, 63, 208-222.
Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43.
Kumar, A., Sharma, K., Singh, H., Naugriya, S. G., Gill, S. S., & Buyya, R. (2021). A drone-based networked system and methods for combating the coronavirus disease (COVID-19) pandemic. Future Generation Computer Systems, 115, 1-19.
Zhang, T., Sodhro, A. H., Luo, Z., Zahid, N., Nawaz, M. W., Pirbhulal, S., & Muzammal, M. (2020). A joint deep learning and internet of medical things driven framework for elderly patients. IEEE Access, 8, 75822-75832.
Islam, M., Mahmud, S., Muhammad, L. J., Nooruddin, S., & Ayon, S. I. (2020). Wearable technology to assist the patients infected with novel coronavirus (COVID-19). SN computer science, 1(6), 1-9.
Wang, J., Han, K., Alexandridis, A., Chen, Z., Zilic, Z., Pang, Y., ... & Piccialli, F. (2020). A blockchain-based eHealthcare system interoperating with WBANs. Future Generation computer systems, 110, 675-685.
Khan, M. A., & Algarni, F. (2020). A healthcare monitoring system for diagnosing heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access, 8, 122259-122269.
Abdellatif, A. A., Mohamed, A., Chiasserini, C. F., Tlili, M., & Erbad, A. (2019). Edge computing for smart health: Context-aware approaches, opportunities, and challenges. IEEE Network, 33(3), 196-203.
Chandra, S., Gupta, R., Ghosh, S., & Mondal, S. (2022). An intelligent and power-efficient biomedical sensor node for wireless cardiovascular health monitoring. IETE Journal of Research, 68(1), 456-466.
Wu, T., Wu, F., Qiu, C., Redouté, J. M., & Yuce, M. R. (2020). A rigid-flex wearable health monitoring sensor patch for IoT-connected healthcare applications. IEEE Internet of Things Journal, 7(8), 6932-6945.
Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2020). CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access, 8, 52541-52549.
Harimoorthy, K., & Thangavelu, M. (2021). Multi-disease prediction model using improved SVM-radial bias technique in the healthcare monitoring system. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3715-3723.
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