Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model

Research Article
Open access

Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model

Jingxuan Zhang 1*
  • 1 New York University    
  • *corresponding author jz4495@nyu.edu
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/38/20230558
ACE Vol.38
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-301-2
ISBN (Online): 978-1-83558-302-9

Abstract

The global pandemic COVID-19 erupted and infected an estimated 10% of the world’s population. Since vaccinations greatly reduced hospitalization rates, most countries removed the restrictive policies implemented to combat the virus. It has become a rather common illness with more than twelve thousand active hospitalizations. As a result, convenient COVID-19 diagnosis from diseases that display overlapping symptoms has become increasingly important. An effective method for patient self-diagnosis greatly reduces hospital presentation, saving time and medical resources. This study uses machine learning techniques to classify and predict several common respiratory diseases quickly and accurately. The author trains several machine learning models that attempt to predict four diseases based on their distinct clinical signs. An open-access database on Kaggle developed for this disease classification is selected and further processed via principal component analysis to decrease database dimension and pinpoint critical symptoms. Support Vector Machine Classifier (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models are used, and their performances are compared. Study results show that the LR model slightly outperforms the others. In conclusion, the effectiveness of the proposed method is proved for classifying the symptoms of patients with allergies, colds, flu, and Covid-19 in this study.

Keywords:

Machine Learning, COVID-19, Overlapping Symptoms, Respiratory

Zhang,J. (2024). Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model. Applied and Computational Engineering,38,237-241.
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References

[1]. Zhao W Jiang W Qiu X 2021 Deep learning for COVID-19 detection based on CT images Sci Rep 11s; p 14353

[2]. Rabie A H Mohamed A M Abo-Elsoud M A Saleh A I 2023 A new Covid-19 diagnosis strategy using a modified KNN classifier Neural Comput Appl 2: pp 1-25

[3]. Aslan M F Sabanci K Durdu A Unlersen M F 2022 COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization Comput Biol Med 142: p 105244

[4]. Xia Y Chen W Ren H et. al. 2021 A rapid screening classifier for diagnosing COVID-19 Int J Biol Sci 17(2): pp 539-548

[5]. Guo X Li Y Li H et. al. An improved multivariate model that distinguishes COVID-19 from seasonal flu and other respiratory diseases Aging (Albany NY) 12(20): pp 19938-19944

[6]. Zhou X Wang Z Li S 2021 Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study Risk Manag Healthc Policy 14: pp 595-604

[7]. Belkacem Abdelkader Nasreddine 2021 End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework Frontiers in Medicine 8

[8]. Islam R Abdel-Raheem E Tarique M 2022 A study of using cough sounds and deep neural networks for the early detection of Covid-19 Biomed Eng Adv 3: p 100025

[9]. Chen J Pan Y Li G et. al. 2021 Distinguishing between COVID-19 and influenza during the early stages by measurement of peripheral blood parameters J Med Virol 93(2): pp 1029-1037

[10]. Alemi F Vang J Wojtusiak J et al. 2022 Differential diagnosis of COVID-19 and influenza PLOS Global Public Health 2(7): p e0000221

[11]. Walter Conway0 2021 COVID, FLU, COLD Symptoms. Kaggle. https://www.kaggle.com/datasets/walterconway/covid-flu-cold-symptoms

Cite this article

Zhang,J. (2024). Clinical diagnosis of overlapping symptoms in COVID-19 based on machine learning model. Applied and Computational Engineering,38,237-241.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-301-2(Print) / 978-1-83558-302-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.38
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhao W Jiang W Qiu X 2021 Deep learning for COVID-19 detection based on CT images Sci Rep 11s; p 14353

[2]. Rabie A H Mohamed A M Abo-Elsoud M A Saleh A I 2023 A new Covid-19 diagnosis strategy using a modified KNN classifier Neural Comput Appl 2: pp 1-25

[3]. Aslan M F Sabanci K Durdu A Unlersen M F 2022 COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization Comput Biol Med 142: p 105244

[4]. Xia Y Chen W Ren H et. al. 2021 A rapid screening classifier for diagnosing COVID-19 Int J Biol Sci 17(2): pp 539-548

[5]. Guo X Li Y Li H et. al. An improved multivariate model that distinguishes COVID-19 from seasonal flu and other respiratory diseases Aging (Albany NY) 12(20): pp 19938-19944

[6]. Zhou X Wang Z Li S 2021 Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study Risk Manag Healthc Policy 14: pp 595-604

[7]. Belkacem Abdelkader Nasreddine 2021 End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework Frontiers in Medicine 8

[8]. Islam R Abdel-Raheem E Tarique M 2022 A study of using cough sounds and deep neural networks for the early detection of Covid-19 Biomed Eng Adv 3: p 100025

[9]. Chen J Pan Y Li G et. al. 2021 Distinguishing between COVID-19 and influenza during the early stages by measurement of peripheral blood parameters J Med Virol 93(2): pp 1029-1037

[10]. Alemi F Vang J Wojtusiak J et al. 2022 Differential diagnosis of COVID-19 and influenza PLOS Global Public Health 2(7): p e0000221

[11]. Walter Conway0 2021 COVID, FLU, COLD Symptoms. Kaggle. https://www.kaggle.com/datasets/walterconway/covid-flu-cold-symptoms

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