University of Oulu

Oluwafemi Samson BALOGUN, Sunday Adewale OLALEYE, Mazhar MOHSIN and Pekka TOIVANEN “Investigating Machine Learning Methods for Tuberculosis Risk Factors Prediction – A Comparative Analysis and Evaluation” Proceedings of the 37th International Business Information Management Association (IBIMA), ISBN: 978-0-9998551-6-4, 1-2 April 2021, Cordoba, Spain

Investigating machine learning methods for tuberculosis risk factors prediction : a comparative analysis and evaluation

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Author: Balogun, Oluwafemi Samson1; Olaleye, Sunday Adewale2; Mohsin, Mazhar1;
Organizations: 1School of Computing, University of Eastern, Finland
2Department of Marketing, Management and International Business, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
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Language: English
Published: International Business Information Management Association, 2021
Publish Date: 2021-05-18


Tuberculosis (TB) is a killer disease, and its root can be traced to Mycobacterium tuberculosis. As the world population increases, the burden of tuberculosis is growing along. Low-and-middle-income nations are not exempted from the tuberculosis crisis. Due to a shortage of medical supplies, tuberculosis bacteria have become a huge public health concern. This study reviewed recent literature from 2015 to 2020 to critically examine what earlier researchers have done about TB burden and treatment. The data used were based on the hospital’s medical department’s record and used a machine-learning algorithm to predict and determine the risk factors associated with the disease. Furthermore, it developed five predictive models to offer the medical managers a valid alternative to the manual estimation of TB patients’ status as cured or not cured. The overall classification showed that all the classification methods performed well for classifying the TB treatment outcome (ranging between 67.5% and 73.4%). Our findings showed that MLP (testing) is the best model to predict TB patients’ treatment outcomes. Age and length of stay were identified as significant risk factors for TB patients in this study. This study explains the study’s limitation, contributions, managerial implications, and suggest future work.

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Series: International Business Information Management Association Conference
ISSN: 2767-9640
ISSN-E: 2767-9640
ISSN-L: 2767-9640
ISBN: 978-0-9998551-6-4
Pages: 1056 - 1070
Host publication: Proceedings of the 37th International Business Information Management Association (IBIMA)
Conference: IBIMA Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 112 Statistics and probability
113 Computer and information sciences
3142 Public health care science, environmental and occupational health
Copyright information: © 2021 The Authors. Published by International business information management association. Creative Commons Attribution License 4.0 Unported.