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The studies and surveys that are already in existence in the field of data analysis have found that there is very complicated and undisturbed correlation between the daily human incidental movements and the identification of the health conditions. The smart health system has been the field of the interest within the researchers and analysts for such a long period of time. Health conditions are so complicated to predict because of their higher volatile and unpredictable nature which is completely dependent on the factors like ever changing health conditions of different people in different places of the world and so on. Predicting the health conditions depending up on the data that is already gathered which is named as the health information alone has become not so sufficient to predict the ups and downs of the health conditions. Numerous analyses regarding the health conditions studies have been conducted in order to attain the accuracy by utilizing many algorithms like naïve bayes regression, data analytics and also the deep learning. In this research paper, we have tried to accelerate the rate of accuracy of the smart health care systems by collecting huge amount of time series data and for the sake of analyzing the data with the help of data analytics models to predict the of health conditions.
Keywords:
Data Analytics; Random Forest; SVM; ANN; K-Nearest
Cite Article:
"Smart Health Care System Using Big Data Analytics", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 11, page no.244 - 248, November-2023, Available :http://www.ijrti.org/papers/IJRTI2311034.pdf
Downloads:
000205173
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator