Page 143 - IJEEE-2022-Vol18-ISSUE-1

Received: 12 March 2022 Revised: 18 April 2022 Accepted: 23 April 2022
DOI: 10.37917/ijeee.18.1.15
Vol. 18| Issue 1| June 2022
? Open Access

Iraqi Journal for Electrical and Electronic Engineering

Original Article

A Comparison of COIVD-19 Cases Classification
Based on Machine Learning Approaches

Oqbah Salim Atiyah*, Saadi Hamad Thalij
College of computer science, University of Tikrit, Iraq

Correspondence
* Oqbah Salim Atiyah
Computer Science College,
University of Tikrit, Tikrit, Iraq
Email: oqbah.s.atiyah35529@st.tu.edu.iq

Abstract
COVID-19 emerged in 2019 in china, the worldwide spread rapidly, and caused many injuries and deaths among humans.
Accurate and early detection of COVID-19 can ensure the long-term survival of patients and help prohibit the spread of the
epidemic. COVID-19 case classification techniques help health organizations quickly identify and treat severe cases.
Algorithms of classification are one the essential matters for forecasting and making decisions to assist the diagnosis, early
identification of COVID-19, and specify cases that require to intensive care unit to deliver the treatment at appropriate timing.
This paper is intended to compare algorithms of classification of machine learning to diagnose COVID-19 cases and measure
their performance with many metrics, and measure mislabeling (false-positive and false-negative) to specify the best algorithms
for speed and accuracy diagnosis. In this paper, we focus onto classify the cases of COVID-19 using the algorithms of machine
learning. we load the dataset and perform dataset preparation, pre-processing, analysis of data, selection of features, split of
data, and use of classification algorithm. In the first using four classification algorithms, (Stochastic Gradient Descent, Logistic
Regression, Random Forest, Naive Bayes), the outcome of algorithms accuracy respectively was 99.61%, 94.82%
,98.37%,96.57%, and the result of execution time for algorithms respectively were 0.01s, 0.7s, 0.20s, 0.04. The Stochastic
Gradient Descent of mislabeling was better. Second, using four classification algorithms, (eXtreme-Gradient Boosting,
Decision Tree, Support Vector Machines, K_Nearest Neighbors), the outcome of algorithms accuracy was 98.37%, 99%, 97%,
88.4%, and the result of execution time for algorithms respectively were 0.18s, 0.02s, 0.3s, 0.01s. The Decision Tree of
mislabeling was better. Using machine learning helps improve allocate medical resources to maximize their utilization.
Classification algorithm of clinical data for confirmed COVID-19 cases can help predict a patient's need to advance to the ICU
or not need by using a global dataset of COVID-19 cases due to its accuracy and quality.
KEYWORDS: COVID-19, Classification algorithm, Prediction, Machine learning.

I. INTRODUCTION disease to avert spreading it. In healthcare ML is important,
it is utilized for collecting data of injures and analyzed using
Coronavirus is known as COVID-19, a new epidemic that algorithms for best learn of the method that COVID-19
appeared in 2019 in Wuhan city in China, this epidemic transition, and refines the velocity and accuracy of prognosis,
spread worldwide very rapidly, and it became a concern for it possible to exist those most infections of threatened depend
all countries due caused to many injuries and deaths[1]. on a personal genetic and physiologic, and ameliorate quality
World-Health-Organization (WHO) proclaimed an treatment ways [3],[4]. ML can learn and develop
emergency status after the COVID-19 spread in most automatically based on experience and knowledge without
countries, this requirement applies strict steps to control and being programmed explicitly. The algorithms really depend
decrease the danger of the epidemic. The virus is spread by on attributes. A big and complex magnitude of the data can
the respiratory system or when an injured person comes into be optimal utilization using ML- algorithms. Machine
contact with an uninjured person [2], the symptom appears learning is useful in the classification, diagnosis, and
on the injured for a duration of (2 - 14) days depending on forecasting the diseases [1]. ML algorithms can predict of the
WHO information, the symptom in a moderate state of number possible confirmed injuries of COVID-19 and the
injures; fever, dry-cough, fatigue, and of the critical state: number of likely deaths in the future [5]. In this work. We
fever, asphyxia, tiredness, and breath distress[3]. With the compare the machine learning approaches in classifying
spread of COVID-19 rapidly there is required to use COVID-19 cases into cases that require to Intensive-Care-
Machine-Learning (ML) to help in the early disclosure of the

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution
and reproduction in any medium, provided the original work is properly cited.
© 2022 The Authors. Iraqi Journal for Electrical and Electronic Engineering by College of Engineering, University of Basrah.

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