Open AccessPrediction of COVID-19 Patient using Supervised Machine Learning Algorithm
Author(s) -
M. Buvana,
K. Muthumayil
Publication year - 2021
Publication title -
sains malaysiana
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 29
eISSN - 2735-0118
pISSN - 0126-6039
DOI - 10.17576/jsm-2021-5008-28
Subject(s) - machine learning , support vector machine , artificial intelligence , computer science , random forest , naive bayes classifier , lasso (programming language) , covid-19 , logistic regression , decision tree , data mining , medicine , disease , pathology , world wide web , infectious disease (medical specialty)
One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.