A Survey on Smart Digital Health Care Record with Prediction of Health Condition

Authors

  • Vishakha Tapkir  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Gopal Mule  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Aishwarya Tingre  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Saurabh Nangare  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India
  • Dr. Sunil Rathod  Professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Maharashtra India

Keywords:

Healthcare, Health Card, QR Code, Prediction, Methodology.

Abstract

Humans are known to be the most intelligent species on the earth and are inherently more health conscious. Since Centuries mankind has discovered various healthcare systems. To automate the process and predict diseases more correctly machine learning methods are attending popularity in research community. We need to implement machine learning methodologies to identify the best-predicted values related to the patients in their respected health condition and also need to analyze the previous health records. For that, we need to maintain a repository or the warehouse where we need to maintain digital data related to the patients and their treatment.

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International Journal of Scientific Research in Science and Technology

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Published

2020-12-18

Issue

Volume 5, Issue 8, November-December-2020

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vishakha Tapkir, Gopal Mule, Aishwarya Tingre, Saurabh Nangare, Dr. Sunil Rathod, " A Survey on Smart Digital Health Care Record with Prediction of Health Condition, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 8, pp.278-283, November-December-2020.