Healthcare Monitoring using Machine Learning Based Data Analytics
In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.
Souri, A., Ghafour, M. Y., Ahmed, A. M., Safara, F., Yamini, A., & Hoseyninezhad, M. (2020). A new machine learning-based healthcare monitoring model for student's condition diagnosis in Internet of Things environment. Soft Computing, 24, 17111-17121.
Gondalia, A., Dixit, D., Parashar, S., Raghava, V., Sengupta, A., & Sarobin, V. R. (2018). IoT-based healthcare monitoring system for war soldiers using machine learning. Procedia computer science, 133, 1005-1013.
Woods, M., & Miklencicova, R. (2021). Digital Epidemiological Surveillance, Smart Telemedicine Diagnosis Systems, and Machine Learning-based Real-Time Data Sensing and Processing in COVID-19 Remote Patient Monitoring. American Journal of Medical Research, 8, 65-78.
Ramkumar, P. N., Haeberle, H. S., Ramanathan, D., Cantrell, W. A., Navarro, S. M., Mont, M. A., & Patterson, B. M. (2019). Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. The Journal of arthroplasty, 34, 2253-2259.
Rghioui, A., Lloret, J., Sendra, S., & Oumnad, A. (2020, September). A smart architecture for diabetic patient monitoring using machine learning algorithms. In Healthcare (Vol. 8, No. 3, p. 348). MDPI.
Anuar, H., & Leow, P. L. (2019, July). Non-invasive core body temperature sensor for continuous monitoring. In 2019 IEEE International Conference on Sensors and Nanotechnology (pp. 1-4). IEEE.
Huang, P. W., Chang, T. H., Lee, M. J., Lin, T. M., Chung, M. L., & Wu, B. F. (2016, November). An embedded non-contact body temperature measurement system with automatic face tracking and neural network regression. In 2016 International Automatic Control Conference (CACS) (pp. 161-166). IEEE.
Huang, M., Tamura, T., Tang, Z., Chen, W., & Kanaya, S. (2016). A wearable thermometry for core body temperature measurement and its experimental verification. IEEE journal of biomedical and health informatics, 21, 708-714.
Rahaman, A., Islam, M. M., Islam, M. R., Sadi, M. S., & Nooruddin, S. (2019). Developing IoT Based Smart Health Monitoring Systems: A Review. Rev. d'Intelligence Artif., 33, 435-440.
Albahri, A. S., Alwan, J. K., Taha, Z. K., Ismail, S. F., Hamid, R. A., Zaidan, A. A., & Alsalem, M. A. (2021). IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. Journal of Network and Computer Applications, 173, 102873.
Paganelli, A. I., Velmovitsky, P. E., Miranda, P., Branco, A., Alencar, P., Cowan, D., & Morita, P. P. (2022). A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home. Internet of Things, 18, 100399.
D. N. V. S. L. S. Indira, Rajendra Kumar Ganiya, P. Ashok Babu, A. Jasmine Xavier, L. Kavisankar, S. Hemalatha, V. Senthilkumar, T. Kavitha, A. Rajaram, Karthik Annam, Alazar Yeshitla, "Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis", BioMed Research International, vol. 2022, Article ID 7799812, 10 pages, 2022. https://doi.org/10.1155/2022/7799812.
Al Bassam, N., Hussain, S. A., Al Qaraghuli, A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021). IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19. Informatics in medicine unlocked, 24, 100588.
Kadhim, K. T., Alsahlany, A. M., Wadi, S. M., & Kadhum, H. T. (2020). An overview of patient's health status monitoring system based on Internet of Things (IoT). Wireless Personal Communications, 114, 2235-2262.
Bhatia, M., Kaur, S., & Sood, S. K. (2020). IoT-inspired smart home based urine infection prediction. Journal of Ambient Intelligence and Humanized Computing, 1-15.
Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., & Li, X. (2021). A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Networks and Applications, 26, 234-252.
Kondaka, L. S., Thenmozhi, M., Vijayakumar, K., & Kohli, R. (2021). An intensive healthcare monitoring paradigm by using IoT based machine learning strategies. Multimedia Tools and Applications, 1-15.
Karthik, A., MazherIqbal, J.L. Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder. Wireless Pers Commun 119, 1959-1973 (2021). https://doi.org/10.1007/s11277-021-08313-6.
Onasanya, A., & Elshakankiri, M. (2021). Smart integrated IoT healthcare system for cancer care. Wireless Networks, 27, 4297-4312.
Uslu, B. Ç., Okay, E., & Dursun, E. (2020). Analysis of factors affecting IoT-based smart hospital design. Journal of Cloud Computing, 9, 1-23.
Wan, J., AAH Al-awlaqi, M., Li, M., O'Grady, M., Gu, X., Wang, J., & Cao, N. (2018). Wearable IoT enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018, 1-10.
Copyright (c) 2023 S.R. Janani, R. Subramanian, S. Karthik, C. Vimalarani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.