Fuzzy regression based patient life risk rate prediction using oxygen level, pulse rate and respiration rate in Covid-19 pandemic (FRPRPS)

Gaurav Kant Shankhdhar, Himanshu Pandey, Atul Kumar Pal, Sumit Mishra

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Today, the general situation worldwide is that the hospitals, sanatoriums and medical colleges are running out of beds, oxygen, medical staff, ventilators and other required paraphernalia that is mandatory for the treatment of the vicious pandemic [1]. The requirement is for a system that takes in some input parameters like Oxygen level of the patient, pulse rate and respiration rate and in turn predicts the Life Risk Rate of that patient [2]. The model used here is a fuzzy regression model that gives the prediction of Life Risk Rate between 1 and 10 units. The lower the predicted Life Risk Rate, the better the chances of survival of the Covid patient. But if the predicted Life Risk Rate is more than the mean of the observations of the Risk in the dataset, then immediate emergency is needed. The benefit of this system is that the patients requiring immediate admission and treatment can be filtered and medical aid in hospital be thereby provided for critical patients. Rest may be home quarantined and domestic medical aid may be given to them until in some unfortunate situation their Risk Rate is near alarming. This paper aims to provide some help in this crucial situation.

Original languageEnglish
Pages (from-to)81-98
Number of pages18
JournalReliability: Theory and Applications
Volume16
Issue number3
DOIs
StatePublished - 1 Sep 2021
Externally publishedYes

Keywords

  • Covid
  • Fuzzy
  • Oxygen Level
  • Prediction
  • Pulse rate
  • Regression

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Statistics, Probability and Uncertainty

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