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Original Article
Beyond Gradient Boosting: A Comparative Deep Learning Framework for Binary Symptom-Based Tuberculosis Screening Using Tabular Neural Architectures and Explainable AI
Suresh S1
Dhanalakshmi S2
1 Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India. 2 Department of Software Systems and AIML, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India
Published Online: July-August 2026
Pages: 239-255
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260604028References
1. World Health Organization, "Global Tuberculosis Report 2025," WHO, Geneva, Switzerland, 2025.
2. K. K. K. Htet, V. Chongsuvivatwong, and S. T. Aung, "Sensitivity and specificity of tuberculosis signs and symptoms screening and
adjunct role of social pathology characteristics in predicting bacteriologically confirmed tuberculosis in Myanmar," Tropical Medicine
and Health, vol. 49, no. 1, pp. 1–16, Jan. 2021.3. S. Suresh, "Prediction of tuberculosis by symptom data through machine learning: Comparative analysis," presented at the NIT Trichy ×
SHoDH – Convergence 2026: Two-Day Zonal Conference on Emerging Multi-disciplinary Research Trends and Advanced
Methodologies for Viksit Bharat 2047, National Institute of Technology Tiruchirappalli, India, Feb. 28–Mar. 1, 2026. Manuscript
accepted; proceedings in press.
4. G. Landry, R. N. Malumba, F. C. B. Kabutakapua, and B. B. Mangata, "Performance comparison of classical algorithms and deep neural
networks for tuberculosis prediction," Jurnal Techno Nusa Mandiri, vol. 21, no. 2, pp. 126–133, Sep. 2024.
5. A. Abdelaziz and P. K. Dutta, "Improving Tuberculosis Diagnosis and Forecasting Through Machine Learning Techniques: A System atic
Review," Multicriteria Optimization: Research and Applications, vol. 1, no. 1, pp. 35–44, Jan. 2024.
6. "A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis," ScienceDirect,
2024.
7. "A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection," Scientific Reports, 2024.
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Systems (DLRS), Boston, MA, USA, 2016, pp. 7–10.
9. S. Ö. Arik and T. Pfister, "TabNet: Attentive Interpretable Tabular Learning," in Proc. AAAI Conf. on Artificial Intelligence, vol. 35, no.
8, 2021, pp. 6679–6687.
10. S. Popov, S. Morozov, and A. Babenko, "Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data," in Proc. Int. Conf.
on Learning Representations (ICLR), 2020.
11. X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin, "TabTransformer: Tabular Data Modeling Using Contextual Embeddings,"
arXiv:2012.06678, 2020.
12. Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, "Revisiting Deep Learning Models for Tabular Data," in Advances in Neural
Information Processing Systems (NeurIPS), vol. 34, 2021.
13. Y. Gorishniy, A. Kotelnikov, and A. Babenko, "TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling,"
arXiv:2410.24210, 2024.
14. "A Survey on Deep Tabular Learning," arXiv:2410.12034, 2024.
15. "A Closer Look at Deep Learning Methods on Tabular Datasets," arXiv:2407.00956, 2024.
16. L. Grinsztajn, E. Oyallon, and G. Varoquaux, "Why do tree-based models still outperform deep learning on typical tabular data?," in Proc.
NeurIPS Datasets and Benchmarks Track, 2022.
17. "Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography," PMC, 2024.
18. "Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: a systematic review of imaging analysis through
deep learning," Frontiers in Microbiology, 2024.
19. "Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention
in developing countries," PMC, 2024.
20. "Comparative analysis of machine learning algorithms for tuberculosis classification based on symptom data," Journal Focus Action of
Research Mathematic (Factor M), 2024.
21. "An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection," arXiv:2510.18819, 2025.
22. E. A. Devi et al., "A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning," in Proc. Int. Conf. on
Electronics and Renewable Systems (ICEARS), Mar. 2022, pp. 1416–1421.
23. S. Roobini, M. S. Kavitha, and S. Karthik, "A systematic review on Machine learning and Neural Network based models for disease
prediction," Journal of Integrated Science and Technology, Feb. 2024.
24. S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, 2017.
25. "Explainable AI in Healthcare: Systematic Review of Clinical Decision Support Systems," medRxiv, Aug. 2024.
26. "XAI-Based Clinical Decision Support Systems: A Systematic Review," Applied Sciences, vol. 14, no. 15, Art. 6638, 2024.
27. "Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour," npj Digital Medicine, 2025.
28. "Improving Explainability and Integrability of Medical AI to Promote Trustworthy Adoption: Review," Journal of Medical Intern et
Research, 2025.
29. S. Suresh, "Tuberculosis Symptom Data for ML [Data set]," Kaggle, 2024. [Online]. Available:
https://doi.org/10.34740/KAGGLE/DSV/9854702
30. A. Vaswani et al., "Attention Is All You Need," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
31. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in Proc. Int. Conf. on Learning Representations (ICLR), 2015.
32. G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. M. Moons, "Transparent Reporting of a Multivariable Prediction Model fo r
Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD Statement," Diabetic Medicine, vol. 32, pp. 146–154, Feb. 2015.
2. K. K. K. Htet, V. Chongsuvivatwong, and S. T. Aung, "Sensitivity and specificity of tuberculosis signs and symptoms screening and
adjunct role of social pathology characteristics in predicting bacteriologically confirmed tuberculosis in Myanmar," Tropical Medicine
and Health, vol. 49, no. 1, pp. 1–16, Jan. 2021.3. S. Suresh, "Prediction of tuberculosis by symptom data through machine learning: Comparative analysis," presented at the NIT Trichy ×
SHoDH – Convergence 2026: Two-Day Zonal Conference on Emerging Multi-disciplinary Research Trends and Advanced
Methodologies for Viksit Bharat 2047, National Institute of Technology Tiruchirappalli, India, Feb. 28–Mar. 1, 2026. Manuscript
accepted; proceedings in press.
4. G. Landry, R. N. Malumba, F. C. B. Kabutakapua, and B. B. Mangata, "Performance comparison of classical algorithms and deep neural
networks for tuberculosis prediction," Jurnal Techno Nusa Mandiri, vol. 21, no. 2, pp. 126–133, Sep. 2024.
5. A. Abdelaziz and P. K. Dutta, "Improving Tuberculosis Diagnosis and Forecasting Through Machine Learning Techniques: A System atic
Review," Multicriteria Optimization: Research and Applications, vol. 1, no. 1, pp. 35–44, Jan. 2024.
6. "A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis," ScienceDirect,
2024.
7. "A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection," Scientific Reports, 2024.
8. H.-T. Cheng et al., "Wide & Deep Learning for Recommender Systems," in Proc. 1st Workshop on Deep Learning for Recommender
Systems (DLRS), Boston, MA, USA, 2016, pp. 7–10.
9. S. Ö. Arik and T. Pfister, "TabNet: Attentive Interpretable Tabular Learning," in Proc. AAAI Conf. on Artificial Intelligence, vol. 35, no.
8, 2021, pp. 6679–6687.
10. S. Popov, S. Morozov, and A. Babenko, "Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data," in Proc. Int. Conf.
on Learning Representations (ICLR), 2020.
11. X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin, "TabTransformer: Tabular Data Modeling Using Contextual Embeddings,"
arXiv:2012.06678, 2020.
12. Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, "Revisiting Deep Learning Models for Tabular Data," in Advances in Neural
Information Processing Systems (NeurIPS), vol. 34, 2021.
13. Y. Gorishniy, A. Kotelnikov, and A. Babenko, "TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling,"
arXiv:2410.24210, 2024.
14. "A Survey on Deep Tabular Learning," arXiv:2410.12034, 2024.
15. "A Closer Look at Deep Learning Methods on Tabular Datasets," arXiv:2407.00956, 2024.
16. L. Grinsztajn, E. Oyallon, and G. Varoquaux, "Why do tree-based models still outperform deep learning on typical tabular data?," in Proc.
NeurIPS Datasets and Benchmarks Track, 2022.
17. "Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography," PMC, 2024.
18. "Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: a systematic review of imaging analysis through
deep learning," Frontiers in Microbiology, 2024.
19. "Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention
in developing countries," PMC, 2024.
20. "Comparative analysis of machine learning algorithms for tuberculosis classification based on symptom data," Journal Focus Action of
Research Mathematic (Factor M), 2024.
21. "An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection," arXiv:2510.18819, 2025.
22. E. A. Devi et al., "A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning," in Proc. Int. Conf. on
Electronics and Renewable Systems (ICEARS), Mar. 2022, pp. 1416–1421.
23. S. Roobini, M. S. Kavitha, and S. Karthik, "A systematic review on Machine learning and Neural Network based models for disease
prediction," Journal of Integrated Science and Technology, Feb. 2024.
24. S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, 2017.
25. "Explainable AI in Healthcare: Systematic Review of Clinical Decision Support Systems," medRxiv, Aug. 2024.
26. "XAI-Based Clinical Decision Support Systems: A Systematic Review," Applied Sciences, vol. 14, no. 15, Art. 6638, 2024.
27. "Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour," npj Digital Medicine, 2025.
28. "Improving Explainability and Integrability of Medical AI to Promote Trustworthy Adoption: Review," Journal of Medical Intern et
Research, 2025.
29. S. Suresh, "Tuberculosis Symptom Data for ML [Data set]," Kaggle, 2024. [Online]. Available:
https://doi.org/10.34740/KAGGLE/DSV/9854702
30. A. Vaswani et al., "Attention Is All You Need," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
31. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in Proc. Int. Conf. on Learning Representations (ICLR), 2015.
32. G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. M. Moons, "Transparent Reporting of a Multivariable Prediction Model fo r
Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD Statement," Diabetic Medicine, vol. 32, pp. 146–154, Feb. 2015.
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