Survey on Machine Learning based E-Health System for Disease Prediction
Keywords:
Cloud Computing, Disease Prediction, Health Id Generation, Machine Learning Algorithm.Abstract
The health reports of the people including diagnostics information and medical prescriptions are provided in the form of test-based case notes due to this the previous health conditions and the medicines used by the person are not known when they visit the hospital later. But storing all the health information of a person in the cloud as the soft copy reduces this problem. To achieve every hospital, dispensary, laboratory must have an internet connection for registration of patient’s data, each patient will be identified by the unique Health ID and all the data of the patient will be stored in the cloud and the data can be accessed by only the particular patient. Accurate and on-time analysis of any health-related problem is important for the prevention and treatment of the illness. To diagnose the disease by accessing all information from linked Health ID with Machine Learning algorithm will boost the system in detection of diseases. Here the work presents review of previous researcher’s techniques used for the prediction of diseases and number of parameters used.
References
- A.U. Haq, J.P. Li, M.H. Memon, S. Nazir, R. Sun, a hybrid intelligent system framework for the prediction of heart disease 2018.
- T. Karayılan, O. Kılı¸c, in “Prediction of heart disease using neural network” 2017 International Conference on Computer Science and Engineering (UBMK) (IEEE, 2017), pp. 719–723
- K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction, Computational and structural biotechnology journal 13, 8 (2015).
- R.D.H.D.P. Sreevalli, K.P.M. Asia, Prediction of diseases using random forest classification algorithm, 2012.
- N. Dong, H. Jonker, and J. Pang, “Challenges in eHealth: from enabling to enforcing privacy,” in Foundations of Health Informatics Engineering and Systems. FHIES 2011. Lecture Notes in Computer Science, Z. Liu and A. Wassyng, Eds., pp. 195–206, Springer, 2011.
- European Network Information Security Agency, An SME Perspective on Cloud Computing, European Network Information Security Agency, Heraklion, Greece, 2009.
- Sathya, M. & Madhan, S. & Jayanthi, K. (2018). Internet of things (IoT) based health monitoring system and challenges. International Journal of Engineering and Technology (UAE). 7. 175-178. 10.14419/ijet. v7i1.7.10645.
- Chaitrali S. Dangare and Sulabha S. Apte, Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications, 2012, 44-48.
- Seshadri Dhruv R., Davies Evan V., Harlow Ethan R., Hsu Jeffrey J., Knighton Shanina C., Walker Timothy A., Voos James E., Drummond Colin K. “Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments”, Frontiers in Digital Health,2020,10.3389/fdgth.2020.00008,2673-253X.
- Mohammadzadeh, N., Gholamzadeh, M., Saeedi, S. et al. The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review. J Ambient Intell Human Comput (2020).
- N. M. Yungaicela-Naula, C. Vargas-Rosales and J. A. Perez-Diaz, "SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning," in IEEE Access, vol. 9, pp. 108495-108512, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.