IoT and machine learning (ML) are becoming increasingly efficient in the medical and telemedicine areas all around the world. This article describes a system that employs latest technology to give a more accurate method of forecasting disease. This technology uses sensors to collect data from the body of the patient. The obtained sensor information is collected with NodeMcU before being transferred to the Cloud Platform "ThinkSpeak" through an ESP8266 Wi-Fi module. ThinkSpeak is a cloud server that provides real-time data streams in the cloud. For the best results, data currently saved in the cloud is evaluated by one of the machine learning algorithms, the KNN algorithm. Based on the findings of the analysis and compared with the data sets, the disease is predicted and a prescription for the relevant disease is issued.

Keywords: IOT, Machine learning, KNN Algorithm, Patient’s body parameters, Sensors.

[1] Uddin, S., Khan, A., Hossain, M. et al. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19, 281. https://doi.org/10.1186/s12911-019-1004-8.

[2] Neumann, J.T., Thao, L.T.P., Callander, E. et al. (2022). Cardiovascular risk prediction in healthy older people. GeroScience, 44: 403-413. https://doi.org/10.1007/s11357-021-00486-z.

[3] Venkatesan, C., Karthigaikumar, P., & Satheeskumaran, S. (2018). Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection. Biomedical Signal Processing and Control, 44: 138-145. https://doi.org/10.1016/j.bspc.2018.04.013.

[4] Thamaraimanalan, T., RA, L., & RM, K. (2021). Multi Biometric Authentication using SVM and ANN Classifiers. Irish Interdisciplinary Journal of Science & Research, 5(1): 118-130.

[5] Venkatesan, C., Karthigaikumar, P., & Varatharajan, R. (2018). A novel LMS algorithm for ECG Signal preprocessing and KNN classifier Based abnormality detection. Multimedia Tools and Applications, 77(8): 10365-10374. https://doi.org/10.1007/s11042-018-5762-6.

[6] Thamaraimanalan T, Sampath P (2019). A low power fuzzy logic based variable resolution ADC for wireless ECG monitoring systems. Cogn Syst Res., 57: 236-245. https://doi.org/10.1016/j.cogsys.2018.10.033.

[7] Balaha, H.M., Shaban, A.O., El-Gendy, E.M. et al. (2022). A multi-variate heart disease optimization and recognition framework. Neural Comput & Applic. https://doi.org/10.1007/s00521-022-07241-1.

[8] Kumar, Y., Koul, A., Singla, R. et al. (2022). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput. https://doi.org/10. 1007/s12652-021-03612-z

[9] Volkov, I., Radchenko, G. & Tchernykh, A. (2021). Digital Twins, Internet of Things and Mobile Medicine: A Review of Current Platforms to Support Smart Healthcare. Program Comput Soft., 47: 578-590. https://doi.org/10. 1134/S0361768821080284.

[10] Tan, L., Yu, K., Bashir, A.K. et al. (2021). Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach. Neural Comput & Applic. https://doi.org/10.1007/s00521-021-06219-9.

[11] Martinho, D., Freitas, A., Sá-Sousa, A. et al. (2021). A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases. J Med Syst., 45, 31. https://doi.org/10.1007/s10916-020-01704-5.

[12] Venkatesan, C., Saravanan, S., & Satheeskumaran, S. (2021). Real-time ECG Signal pre-processing and Neuro fuzzy-based CHD risk prediction. International Journal of Computational Science and Engineering, 24(4), 323. https://doi.org/10.1504/ijcse.2021.10039962.

[13] Jackins, V., Vimal, S., Kaliappan, M. et al. (2021). AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J Supercomput., 77: 5198-5219. https://doi.org/10.1007/s11227-020-03481-x.

[14] Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., & Kumar, R. (2018). ECG signal preprocessing and SVM Classifier-based abnormality detection in Remote healthcare applications. IEEE Access, 6: 9767-9773. https://doi.org/10.1109/access.2018.2794346.

[15] Louridi, N., Douzi, S. & El Ouahidi, B. (2021). Machine learning-based identification of patients with a cardiovascular defect. J Big Data 8, 133. https://doi.org/10.1186/s40537-021-00524-9.