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 patients 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. 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, URL: https://www.ijtsrd.com/papers/ijtsrd46400.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/46400/health-risk-prediction-using-support-vector-machine-with-gray-wolf-optimization-in-covid19-pandemic-crisis/swati-shilpi
International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 6, September-October 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD46400 | Volume – 5 | Issue – 6 | Sep-Oct 2021
Page 230
Health Risk Prediction Using Support Vector Machine with
Gray Wolf Optimization in Covid-19 Pandemic Crisis
Swati Shilpi
1
, Dr. Damodar Prasad Tiwari
2
1PG Scholar, 2Assistant Professor,
1,2Department of CSE, BIST, Bhopal, Madhya Pradesh, India
ABSTRACT
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.
KEYWORDS: Covid-19, Pneumonia, Machine Learning, Artificial
Intelligence, Healthcare
How to cite this paper: Swati Shilpi |
Dr. Damodar Prasad Tiwari "Health
Risk Prediction Using Support Vector
Machine with Gray Wolf Optimization
in