Towards Machine Learning Algorithm for Screening Prediction of COVID-19 Patients
Article Sidebar
Main Article Content
Abstract
his research aims to develop a web-based application for screening COVID-19 patients by applying with machine learning techniques. We have applied the techniques to classify the data and then compare the algorithm’s efficiency in prediction of COVID-19 patient screening. The data on 1,608,923 patients from the Department of Disease Control are accumulated. In particular, the data from January 1, 2020 to October 1, 2021 are learnt by three classification algorithms: Random Forest, Neural network, and Naive Bayes. The performance of predictive techniques between features are compared. The models learnt and generated based on different three algorithms are tested by the Split Test method. The data are randomly divided into two parts: Training Data (80%) and Test Data (20%) by using Orange Canvas. The models are then experimented to determine the performance test results with the highest accuracy. As the results, it is found that the accuracy rates of Random Forest, Neural Network, and Naïve Bayes are 93.33%, 92.7%, and 92.1%, respectively. The model based on Random Forest technique with the highest value of accuracy is then applied in the application for screening COVID-19 patients.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
S. Manmana, S. Iamsirithaworn, and S. Uttayamakul, “Coronavirus Disease-19 (Covid-19),” Journal of Bamrasnaradura Infectious Diseases Institute," vol. 14(2), pp. 124–33, Mar. 2020.
T. Saowapa, P. Supistra, and R. Ranistha, “Nursing care for patients with COVID-19 in the isolation unit, Siriraj Hospital.” Jul. 2020. [Online]. Available: https://dx.doi.org/10.33192/Simedbull.2020.29
T.-N. Rachada, “COVID-19: An Invisible War Against Coronavirus,” THAI FOOD AND DRUG JOURNAL, vol. 27(2), May 2020.
K. Natawan, “Knowledge, Attitudes, and Preventive Behaviors of COVID-19 among People Living in Amphoe U- thong, Suphanburi Province,” Journal of Prachomklao College of Nursing, Phetchaburi Province, vol. 4 No.1, Apr. 2021.
K. L. Trepanning and J. Nammong, “Factors Associated with Preventive Behaviors towards the Coronavirus 2019 (COVID-19) of Employees in a Large Factory Krathumbean District, Samutsakorn Province,” Journal of Nursing, Siam University, 2021.
Kasetsart university, “Covid-19 and epidemiology,” COVID-19 Course, Jul. 2020. https://learningcovid.ku.ac.th/course/?c=3&l=1
Department of disease control, “DDC OPENDATA Covid19 Thailand,” DDC OPENDATA Covid19 Thailand, 2021. https://covid19.ddc.moph.go.th/ (accessed Oct. 22, 2021).
A. Puitrakul, “What is machine learning,” What is machine learning, Jan, 2018. https://arnondora.in.th/what-is-machine-learning/
E. Digest, “Artificial intelligence (AI) -- a catalyst for China’s post-COVID economy,” Kasikorn research center, Jun, 2020. https://www.kasikornresearch.com/th/analysis/k-social-media/Pages/AI-China-FB300620.aspx (accessed Oct. 25, 2021).
S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review,” Chaos, Solitons & Fractals, vol. 139, p. 110059, Oct. 2020, doi: 10.1016/j.chaos.2020.110059.
Department of disease control, “Coronavirus Disease 2019: COVID-19.” Dec. 2563. [Online]. Available: https://ddc.moph.go.th/viralpneumonia/file/g_srrt/g_srrt_241263.pdf
Department of disease control, “Guideline for disease control in quarantine facilities.” Containment Measures Mission Group (QUARANTINE) Emergency Operations Center ,Department of disease control, Jul. 2020. [Online]. Available: https://ddc.moph.go.th/viralpneumonia/file/g_quarantine/g_quarantine_state210763n.pdf
C. Patcharacharoenwong, H. Kankawee, and K. Warangkhana, “Arrival Time Prediction Model to a Pier for Public Transportation Boats,” Journal of Science Ladkrabang, vol. 29(2), 2020, [Online]. Available: https://li01.tci-thaijo.org/index.php/science_kmitl/article/download/241105/169801/
E. Pacharawongsakda, An introduction to Data Mining Techniques, vol. 2. 2014.
A. Thongjit, P. Suksawang, and M. Jatupat, “Development of Data Classification using a Hybrid Method of Adaptive Artificial Neural Networks and Particle Swarm Optimization for Identifying Patients at Risk of Diabetes,” Research Methodology & Cognitive Science, vol. 17(2), Dec. 2019, [Online]. Available: https://so05.tci-thaijo.org/index.php/RMCS/article/download/243179/165072/
P. Duangklang and R. Kruakaew, “Models for automatic aircraft type prediction,” NKRAFA Journal of Science and Technology, 2019.
“Confusion Matrix - an overview | ScienceDirect Topics.” https://www.sciencedirect.com/topics/engineering/confusion-matrix (accessed Jun. 08, 2022).
A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks,” Computers in Biology and Medicine, vol. 121, p. 103795, Jun. 2020, doi: 10.1016/j.compbiomed.2020.103795.
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Computers in Biology and Medicine, vol. 121, p. 103792, Jun. 2020, doi: 10.1016/j.compbiomed.2020.103792.
L. Sun et al., “Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19,” Journal of Clinical Virology, vol. 128, p. 104431, Jul. 2020, doi: 10.1016/j.jcv.2020.104431.
Z. Karhan and F. Akal, “Covid-19 Classification Using Deep Learning in Chest X-Ray Images,” in 2020 Medical Technologies Congress (TIPTEKNO), Antalya, Turkey, Nov. 2020, pp. 1–4. doi: 10.1109/TIPTEKNO50054.2020.9299315.
C. Iwendi et al., “COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm,” Frontiers in Public Health, vol. 8, p. 357, 2020, doi: 10.3389/fpubh.2020.00357.
J. Wu et al., “Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results,” Apr. 2020. doi: 10.1101/2020.04.02.20051136.