Revolutionizing Tuberculosis Prediction: A Cutting-Edge Approach
Received: 4 February 2025 | Revised: 24 February 2025 and 19 March 2025 | Accepted: 22 March 2025 | Online: 4 June 2025
Corresponding author: Priya Dasarwar
Abstract
The diversity of human biological systems and lifestyles requires specialized medical treatments in healthcare. In response to this need, this paper proposes a robust AI-based tuberculosis prediction model using Machine Learning (ML). This model utilizes key patient characteristics and symptoms to predict the presence of the disease using treatment data. For a comprehensive study, the dataset was exposed to a number of algorithms, including Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Of these, RF emerged as the top performer, with an impressive accuracy rate of 99.98%. This remarkable accuracy demonstrates its ability to accurately predict tuberculosis based on symptoms and important variables. By utilizing this paradigm, healthcare systems can significantly improve their diagnostic capabilities, resulting in accurate and rapid disease diagnosis. Overall, this breakthrough represents a tremendous advancement in healthcare technology by creating a promising pathway for precise disease prediction, enabling timely treatment, and improving all aspects of healthcare outcomes.
Keywords:
tuberculosis prediction, machine learning algorithms, comparative analysis, symptom based, precise disease predictionDownloads
References
S. Hansun, A. Argha, S.-T. Liaw, B. G. Celler, and G. B. Marks, "Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review," Journal of Medical Internet Research, vol. 25, no. 1, Jul. 2023, Art. no. e43154.
M. B. Mizan, Md. A. M. Hasan, and S. R. Hassan, "A Comparative Study of Tuberculosis Detection Using Deep Convolutional Neural Network," in 2020 2nd International Conference on Advanced Information and Communication Technology, Dhaka, Bangladesh, 2020, pp. 157–161.
R. Kebache, A. Laouid, S. S. Guia, M. Kara, and N. Bouadem, "Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning," in Proceedings of the 7th International Conference on Future Networks and Distributed Systems, Dubai, United Arab Emirates, 2023, pp. 352–356.
Y. R. Choi, S. H. Yoon, J. Kim, J. Y. Yoo, H. Kim, and K. N. Jin, "Chest Radiography of Tuberculosis: Determination of Activity Using Deep Learning Algorithm," Tuberculosis and Respiratory Diseases, vol. 86, no. 3, pp. 226–233, May 2023.
C. Dasanayaka and M. B. Dissanayake, "Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 9, no. 1, pp. 39–49, Jan. 2021.
X. A. Inbaraj, C. Villavicencio, J. J. Macrohon, J.-H. Jeng, and J.-G. Hsieh, "A Novel Machine Learning Approach for Tuberculosis Segmentation and Prediction Using Chest-X-Ray (CXR) Images," Applied Sciences, vol. 11, no. 19, Oct. 2021, Art. no. 9057.
K. K. Goswami, R. Kumar, R. Kumar, A. J. Reddy, and S. K. Goswami, "Deep Learning Classification of Tuberculosis Chest X-rays," Cureus, vol. 15, no. 7, Jul. 2023, Art. no. e41583.
A. Iqbal, M. Usman, and Z. Ahmed, "An efficient deep learning-based framework for tuberculosis detection using chest X-ray images," Tuberculosis, vol. 136, Sep. 2022, Art. no. 102234.
H. H. Rashidi et al., "Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data," Journal of Pathology Informatics, vol. 13, Jan. 2022, Art. no. 100172.
M. Senthilmurugan, M. Latha, and R. Chinnaiyan, "Analysis and Prediction of Tuberculosis using Machine Learning Classifiers," in 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, Coimbatore, India, 2021, pp. 1–4.
S. N. Chinagudaba et al., "Leveraging machine learning for predictive analysis of tuberculosis treatment outcomes: A comprehensive study using Karnataka TB data," Next Research, vol. 1, no. 1, Oct. 2024, Art. no. 100011.
S. Saralaya, V. Saralaya, J. R. Rodrigues, Prajna, J. M. Cardoza, and M. M. Devadiga, "Identification and Prediction of Type of TB Based on Drug Resistance Using Machine Learning," in 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, Mangalore, India, 2024, pp. 400–403.
A. C. Apunike et al., "Analyses of Public Health Databases via Clinical Pathway Modelling: TBWEB," in Proceedings of the 20th International Conference on Computational Science, ICCS 2020, Part IV, Amsterdam, Netherlands, 2020, pp. 550–562.
M. Karki et al., "Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays," Diagnostics, vol. 12, no. 1, Jan. 2022, Art. no. 188.
R. Mohan, S. Kadry, V. Rajinikanth, A. Majumdar, and O. Thinnukool, "Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm," Life, vol. 12, no. 11, Nov. 2022, Art. no. 1848.
A. Zaman, S. S. Khattak, and Z. Hassan, "Medical Imaging for the Detection of Tuberculosis Using Chest Radio Graphs," in 2019 International Conference on Advances in the Emerging Computing Technologies, Al Madinah Al Munawwarah, Saudi Arabia, 2020, pp. 1–5.
A. M. Alghamdi, M. A. Al-Khasawneh, A. Alarood, and E. Alsolami, "The Role of Machine Learning in Managing and Organizing Healthcare Records," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13695–13701, Apr. 2024.
S. Rajaraman, G. Zamzmi, L. R. Folio, and S. Antani, "Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers," Frontiers in Genetics, vol. 13, Feb. 2022, Art. no. 864724.
S. U. Kiruthika, S. K. S. Raja, V. Balaji, C. J. Raman, and S. S. L. Durai Arumugam, "Detection of Tuberculosis in Chest X-rays using U-Net Architecture," International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, pp. 2514–2519, Nov. 2019.
E. Showkatian, M. Salehi, H. Ghaffari, R. Reiazi, and N. Sadighi, "Deep learning-based automatic detection of tuberculosis disease in chest X-ray images," Polish Journal of Radiology, vol. 87, pp. 118–124, Feb. 2022.
T.-M. Le, B.-T. Nguyen-Tat, and V. M. Ngo, "Automated evaluation of Tuberculosis using Deep Neural Networks," EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 9, no. 30, Apr. 2022, Art. no. e4.
J. Ureta and A. Shrestha, "Identifying drug-resistant tuberculosis from chest X-ray images using a simple convolutional neural network," Journal of Physics: Conference Series, vol. 2071, no. 1, Oct. 2021, Art. no. 012001.
J. Devasia, H. Goswami, S. Lakshminarayanan, M. Rajaram, S. Adithan, and A. Bharanidharan, "Deep Learning Classification of Active Tuberculosis Using Chest X-Rays: Efficacy of Transfer Learning and Generalization Performance of Cross-Population Datasets." Research Square, Jan. 14, 2022.
L. G. C. Evangelista and E. B. Guedes, "Ensembles of Convolutional Neural Networks on Computer-Aided Pulmonary Tuberculosis Detection," IEEE Latin America Transactions, vol. 17, no. 12, pp. 1954–1963, Dec. 2019.
Downloads
How to Cite
License
Copyright (c) 2025 Priya Dasarwar, Uma Yadav, Kevin Morris, Nekita Chavhan, Shweta Bondre, Supriya Kalamkar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
