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Article Content:-
Abstract
The cause of tuberculosis can be dangerous and even be a fatal disorder, the mainstream of patients are able to recover with prompt diagnosis and treatment. After a few weeks of treatment, you won't be contagious, and you could start feeling better and as a result most people don’t take their TB medications as prescribed by their doctor. Also taking TB medications or not completing the entire therapy could lead to the bacteria still alive in them to develop antibiotics resistance, which is far more dangerous and difficult to treat. In this research, two (2) machine learning algorithms; Logistic Regression (LR) and Random Forest (RF) were employed for predicting tuberculosis treatment outcome in order to ensure treatment completion for favorable outcome. GridSearchCV was used to improve the models performance and of the two developed model, both models performed very well with LR having an accuracy of 75%, and RF an accuracy of 55%.
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References
Annabel, B., Anna, D., Hannah, M.: Global Tuberculosis Report 2019. Geneva: World Heal Organ (2019).
World Health Organization. Global tuberculosis report 2017.
https://www.who.int/tb/publications/global_report/gtbr2017_main_text.pdf?u%20a=1
World Health Organization Global tuberculosis report 2019.
http://apps.who.int/iris/bitstream/handle/10665/329368/9789241565714-eng.pdf?ua=1
World Health Organization Global tuberculosis report 2018. https://www.who.int/teams/global-tuberculosisprogramme/tb-reports.
Daniel, P.C., Christy, L.H.: Finding the Missing Tuberculosis Patients. The Journal of Infectious Diseases, Volume 216, Issue suppl_7, 1 October 2017, Pages S675–S678 (2017)
Federal Ministry of Health. (2017). National TB and leprosy control program annual report. Retrieved from www.ntblcp.or.ng/resources
Ekins, S., Godbole, A.A., Kéri, G., Orfi, L., Pato, J., Bhat, R.S., Verma, R., Bradley, E.K., Nagaraja, V.: Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.
Tuberculosis, vol. 103, pp. 52–60, Mar. (2017)
Panch, T., Szolovits, P., Atun, R.: Artificial intelligence, machine learning and health systems. J Glob Health. 2018 Dec;8(2):020303 (2008)
Wang, J., Wang, C., Zhang, W.: Data analysis and forecasting of tuberculosis prevalence rates for smart healthcare based on a novel combination model. Applied sciences, 8(9), 1693 (2018)
Awaysheh, A., Wilcke, J., Elvinger, F., Rees, L., Fan, W., Zimmerman, K.L.: Review of medical decision support and machine-learning methods. Veterinary pathology, 56(4), 512-525 (2019)
Asad, M., Mahmood, A., Usman, M.: (2020) A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries. Tuberculosis .
Balogun, O.S., Olaleye, S.A., Mohsin, M., Toivanen, P.: (2021). Investigating Machine Learning Methods for Tuberculosis Risk Factors Prediction - A Comparative Analysis and Evaluation. Proceedings of the 37th International Business Information Management Association (IBIMA), 2021, 1056-1070
Hayes, N.: (2019).Dynamic Prediction of Treatment Outcomes for Recurrent Tuberculosis Patients. Industrial Engineering Undergraduate Honors
Adeniji, O.D., Adeyemi, S.O, Ajagbe. (2022). Improved Bagging Ensemble in Predicting Mental Disorder Using Hybridized Random Forest - Artificial Neural Network Model. Informatics Vol.46. 543–550.
Adeniji, O. D., Olatunji, O.O (2020).. Zero Day Attack Prediction with Parameter Setting Using Bi Direction Recurrent Neural Network in Cyber Security. International Journal of Computer Science and Information Security IJCSIS Vol. 18. No. 3: 111-118.
Adeyemi, S.O., Ochanya., R.A., Adeniji, O.D. (2022).Classification of Family Planning Services Utilization for Prediction of Social and Economic Factors, International Journal of Advanced Engineering and Management Research Vol. 7, No. 05;
Idowu, I.R, Adeniji O.D.,Shehu, A.E and Togun , O.A.(2021) Prediction of Breast Cancer Images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network. Journal Transactions on Networks and Communications - Vol. 9, No. 25.pp 29-38.