Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries
Journal article
Authors | Karmani, P., Chandio, A. A., Korejo, I. A., Samuel, O. and Aborokbah, M. |
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Abstract | Background: Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a Methods: To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic Results: The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, |
Keywords | Tuberculosis (TB) diagnosis; Machine learning (ML); Good health and well-being; Optimal ML model for TB diagnosis |
Year | 2024 |
Journal | Peer J Computer Science |
Journal citation | pp. 1-20 |
Publisher | PeerJ |
ISSN | 2376-5992 |
Digital Object Identifier (DOI) | https://doi.org/10.7717/peerj-cs.2397 |
Web address (URL) | https://peerj.com/articles/cs-2397/# |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 16 Oct 2024 |
Publication process dates | |
Deposited | 20 Nov 2024 |
https://repository.derby.ac.uk/item/qqz17/machine-learning-based-tuberculosis-ml-tb-health-predictor-model-early-tb-health-disease-prediction-with-ml-models-for-prevention-in-developing-countries
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