Open AccessEvaluation of COVID-19 Cases based on Classification Algorithms in Machine Learning
Author(s) -
Oqbah Salim Atiyah,
Saadi Hamad Thalij
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19326
Subject(s) - stochastic gradient descent , machine learning , random forest , naive bayes classifier , computer science , artificial intelligence , covid-19 , logistic regression , algorithm , statistical classification , decision tree , gradient descent , support vector machine , artificial neural network , disease , medicine , infectious disease (medical specialty) , pathology
COVID-19 has appeared in china, spread rapidly the world wide and caused with many injuries, deaths between humans. It is possible to avoid the spread of the disease or reduce its spread with the machine learning and the diagnostic techniques, where the use classification algorithms are one of the fundamental issues for prediction and decision-making to help of the early detection, diagnose COVID-19 cases and identify dangerous cases that need admit Intensive Care Unit to provide treatment in a timely manner. In this paper, we use the machine learning algorithms to classify the COVID-19 cases, the dataset got from dataset search on google and used four algorithms, as (Logistic Regression, Naive Bayes, Random Forest, Stochastic Gradient Descent), the result of algorithms accuracy was 94.82%, 96.57%, 98.37%, 99.61% respectively and the execution time of each algorithm were 0.7s, 0.04s, 0.20s,0.02s respectively, and with the mislabeling Stochastic Gradient Descent algorithm was better.