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Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis

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Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis


Swati Shilpi | Dr. Damodar Prasad Tiwari



Swati Shilpi | Dr. Damodar Prasad Tiwari "Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid-19 Pandemic Crisis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6, October 2021, pp.230-234, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf

The opinion of disease is important for Covid 19 as the antigen kit and RTPCR are unperfect and should be better for diagnosing such disease. Real-Time Return Transcription (real-time converse transcription – polymerase chain). Healthcare practices include the collection of various sorts of patient data to help the physician diagnose the patient's health. These data could be simple symptoms, first diagnosis by a doctor, or an in-depth laboratory test. These data are therefore used for analyses only by a doctor, who subsequently uses his particular medical skills to found the ailment. In order to classify Covid 19 disease datasets such mild, middle and severe diseases, the proposed model utilizes the notion of controlled machine education and GWO-optimization to regulate if the patient is affecting or not. An efficiency analysis is calculated and compared of disease data for both algorithms. The results of the simulations illustrate the effective nature and complexity of the data set for the grading techniques. Compared to SVM, the suggested model provides 7.8 percent improved prediction accuracy. The prediction accuracy is 8% better than the SVM. This results in an F1 score of 2 percent better than an SVM forecast.

Covid-19, Pneumonia, Machine Learning, Artificial Intelligence, Healthcare


IJTSRD46400
Volume-5 | Issue-6, October 2021
230-234
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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