Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application

Shah Siddiqui; Elias Hossain; S. M. Asaduzzaman; Sabila Al Jannat; Ta seen Niloy; Wahidur Rahman; Shamsul Masum; Adrian Hopgood; Alice Good; Alexander Gegov

doi:10.1007/978-981-19-1012-8_55

Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application

Shah Siddiqui^*, Elias Hossain, S. M. Asaduzzaman, Sabila Al Jannat, Ta seen Niloy, Wahidur Rahman, Shamsul Masum, Adrian Hopgood, Alice Good, Alexander Gegov

^*Corresponding author for this work

Time Research & Innovation (Tri)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

This study is divided into risk factor analysis (RFA) and proposed system architecture (PSA). The light gradient boosting machine (LightGBM) algorithm in the RFA will work with the PSA to predict the risk factors. The results, efficacy, and performance will be validated via a ROC-AUC curve. Therefore, a system usability scale (SUS) procedure will be implemented to increase the performance. If the SUS score reaches 85–99 and 100 thresholds, it will be classified as appropriate for use and robust. The prediction score thresholds will be 0–100. If the score is below 25, it will be classified as normal, 26–50 as moderate, 51–70 risk, and 71–100 as severe. Due to a shortage of experienced staff and intelligent technology, it is becoming progressively difficult to reduce COVID-19 fatality rates. In this research, a lightweight mobile application has been suggested from which the significant patterns and factors can be recognised. Furthermore, it will assist both doctors and patients become aware of COVID-19 risk factors and take the required steps to mitigate them.

Original language	English
Title of host publication	Inventive Systems and Control
Subtitle of host publication	Proceedings of ICISC 2022
Editors	V. Suma, Zubair Baig, Selvanayaki Kolandapalayam Shanmugam, Pascal Lorenz
Publisher	Springer Singapore
Pages	775-788
Number of pages	14
ISBN (Electronic)	9789811910128
ISBN (Print)	9789811910111
DOIs	https://doi.org/10.1007/978-981-19-1012-8_55
Publication status	Published - 2 Aug 2022
Event	6th International Conference on Inventive Systems and Control, ICISC 2022 - Coimbatore, India Duration: 6 Jan 2022 → 7 Jan 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	436
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Conference on Inventive Systems and Control, ICISC 2022
Country/Territory	India
City	Coimbatore
Period	6/01/22 → 7/01/22

Keywords

And system usability scale (SUS)
COVID-19 mortality
machine learning
mobile application
risk factor
ROC-AUC

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10.1007/978-981-19-1012-8_55

Analysing and Identifying COVID-19 Risk Factors using Machine Learning Algorithm with Smartphone Application
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-981-19-1012-8_55.
Accepted author manuscript (Post-print), 597 KB

Cite this

Siddiqui, S., Hossain, E., Asaduzzaman, S. M., Al Jannat, S., Niloy, T. S., Rahman, W., Masum, S., Hopgood, A., Good, A., & Gegov, A. (2022). Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application. In V. Suma, Z. Baig, S. Kolandapalayam Shanmugam, & P. Lorenz (Eds.), Inventive Systems and Control: Proceedings of ICISC 2022 (pp. 775-788). (Lecture Notes in Networks and Systems; Vol. 436). Springer Singapore. https://doi.org/10.1007/978-981-19-1012-8_55

Siddiqui, Shah ; Hossain, Elias ; Asaduzzaman, S. M. et al. / Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application. Inventive Systems and Control: Proceedings of ICISC 2022. editor / V. Suma ; Zubair Baig ; Selvanayaki Kolandapalayam Shanmugam ; Pascal Lorenz. Springer Singapore, 2022. pp. 775-788 (Lecture Notes in Networks and Systems).

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title = "Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application",

abstract = "This study is divided into risk factor analysis (RFA) and proposed system architecture (PSA). The light gradient boosting machine (LightGBM) algorithm in the RFA will work with the PSA to predict the risk factors. The results, efficacy, and performance will be validated via a ROC-AUC curve. Therefore, a system usability scale (SUS) procedure will be implemented to increase the performance. If the SUS score reaches 85–99 and 100 thresholds, it will be classified as appropriate for use and robust. The prediction score thresholds will be 0–100. If the score is below 25, it will be classified as normal, 26–50 as moderate, 51–70 risk, and 71–100 as severe. Due to a shortage of experienced staff and intelligent technology, it is becoming progressively difficult to reduce COVID-19 fatality rates. In this research, a lightweight mobile application has been suggested from which the significant patterns and factors can be recognised. Furthermore, it will assist both doctors and patients become aware of COVID-19 risk factors and take the required steps to mitigate them.",

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Siddiqui, S, Hossain, E, Asaduzzaman, SM, Al Jannat, S, Niloy, TS, Rahman, W, Masum, S , Hopgood, A , Good, A & Gegov, A 2022, Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application. in V Suma, Z Baig, S Kolandapalayam Shanmugam & P Lorenz (eds), Inventive Systems and Control: Proceedings of ICISC 2022. Lecture Notes in Networks and Systems, vol. 436, Springer Singapore, pp. 775-788, 6th International Conference on Inventive Systems and Control, ICISC 2022, Coimbatore, India, 6/01/22. https://doi.org/10.1007/978-981-19-1012-8_55

Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application. / Siddiqui, Shah; Hossain, Elias; Asaduzzaman, S. M. et al.
Inventive Systems and Control: Proceedings of ICISC 2022. ed. / V. Suma; Zubair Baig; Selvanayaki Kolandapalayam Shanmugam; Pascal Lorenz. Springer Singapore, 2022. p. 775-788 (Lecture Notes in Networks and Systems; Vol. 436).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Siddiqui S, Hossain E, Asaduzzaman SM, Al Jannat S, Niloy TS, Rahman W et al. Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application. In Suma V, Baig Z, Kolandapalayam Shanmugam S, Lorenz P, editors, Inventive Systems and Control: Proceedings of ICISC 2022. Springer Singapore. 2022. p. 775-788. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-1012-8_55

Analysing and identifying COVID-19 risk factors using machine learning algorithm with smartphone application

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Keywords

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