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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationInventive Systems and Control
Subtitle of host publicationProceedings of ICISC 2022
EditorsV. Suma, Zubair Baig, Selvanayaki Kolandapalayam Shanmugam, Pascal Lorenz
PublisherSpringer Singapore
Pages775-788
Number of pages14
ISBN (Electronic)9789811910128
ISBN (Print)9789811910111
DOIs
Publication statusPublished - 2 Aug 2022
Event6th International Conference on Inventive Systems and Control, ICISC 2022 - Coimbatore, India
Duration: 6 Jan 20227 Jan 2022

Publication series

NameLecture Notes in Networks and Systems
Volume436
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference6th International Conference on Inventive Systems and Control, ICISC 2022
Country/TerritoryIndia
CityCoimbatore
Period6/01/227/01/22

Keywords

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

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