Skip to main content
SpringerLink
Log in

Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method

  • Conference paper
  • First Online:
Ubiquitous Intelligent Systems (ICUIS 2021)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 302))

Abstract

Tuberculosis (TB) is a disease of human infection that affects the respiratory and other body systems. The WHO reported an average incidence of TB per annum of 2.2 million new cases contracted by individuals. A major public concern for the developing country is to reduce the reproductive rate of transmission and outbreaks of tuberculosis disease. It needs to improve the diagnosis process and encourage patient faithfulness to medical treatment. This research study includes several recent ensemble classification algorithms to choose our base model, achieving high-performance accuracy. The extreme gradient boosting/ XGBoosting model performs the highest testing score of AUC 95.86% using the full optimizing trained model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. P. Dande, P. Samant, Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis, a review. Tuberculosis 108, 1–9 (2018)

    Article  Google Scholar 

  2. E. Winarko, R. Wardoyo, Preliminary diagnosis of pulmonary tuberculosis using ensemble method. in 2015 International Conference on Data and Software Engineering (ICoDSE). (IEEE, 2015), pp. 175–180

    Google Scholar 

  3. S. Natarajan, K.N. Murthy, A data mining approach to the diagnosis of tuberculosis by cascading clustering and classification. arXiv preprint arXiv, (2011), pp. 1108–1045

    Google Scholar 

  4. S.S. Meraj, R. Yaakob, A. Azman, S.N. Rum, A.A. Nazri, Artificial Intelligence in diagnosing tuberculosis: a review. Int. J. Adv. Sci. Eng. Inform. Technol 81–91 (2019)

    Google Scholar 

  5. R. Sarin, V. Vohra, U.K. Khalid, P.P. Sharma, V. Chadha, M.A. Sharada, Prevalence of pulmonary tuberculosis among adults in selected slums of Delhi city. Indian J. Tuberculosis 130–134 (2018)

    Google Scholar 

  6. S. Gupta, V. Arora, O.P. Sharma, L. Satyanarayana, A.K. Gupta, Prevalence and pattern of respiratory diseases including Tuberculosis in elderly in Ghaziabad–Delhi–NCR. Indian J. Tuberculosis 236–41 (2016)

    Google Scholar 

  7. A.B. Suthar, P.K. Moonan, H.L. Alexander, Towards national systems for continuous surveillance of antimicrobial resistance: lessons from tuberculosis, PLoS Med. (2018)

    Google Scholar 

  8. D.J. Horne, M. Kohli, J.S. Zifodya, I. Schiller, N. Dendukuri, D. Tollefson, S.G. Schumacher, E.A. Ochodo, M. Pai, K.R. Steingart, Xpert MTB/RIF and Xpert MTB/RIF ultra for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database of Systemat. Rev. (2019)

    Google Scholar 

  9. J.L. Díaz-Huerta, A. del Carmen Téllez-Anguiano, J.A. Gutiérrez-Gnecchi, O.Y. Colin-González, F.L. Zavala-Santoyo, S. Arellano-Calderón, Image preprocessing to improve Acid-Fast Bacilli (AFB) detection in smear microscopy to diagnose pulmonary tuberculosis. in 2019 International Conference on Electronics, Communications and Computers (CONIELECOMP), (IEEE Press, 2019), pp. 66–73

    Google Scholar 

  10. C.T. Sreeramareddy, Z.Z. Qin, S. Satyanarayana, R. Subbaraman, M. Pai, Delays in diagnosis and treatment of pulmonary tuberculosis in India: a systematic review. Int. J. Tuberculosis Lung Disease 255–266 (2014)

    Google Scholar 

  11. P. Ghosh, D. Bhattacharjee, M. Nasipuri, A hybrid approach to diagnosis of tuberculosis from sputum. in 2016 İnternational Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), (IEEE Press, 2016), pp. 771–776

    Google Scholar 

  12. I. Goni, C.U. Ngene, I. Manga, N. Auwal, J.C. Sunday, Intelligent system for diagnosing tuberculosis using adaptive neuro-fuzzy. Asian J. Res. Comput. Sci. 1–9 (2018)

    Google Scholar 

  13. F.F. Jahantigh, H. Ameri, Evaluation of TB patients characteristics based on predictive data mining approaches. J. Tuberculosis Res. 13–22 (2017)

    Google Scholar 

  14. A.A. Shazzadur Rahman, I. Langley, R. Galliez, A. Kritski, E. Tomeny, S.B. Squire, Modelling the impact of chest X-ray and alternative triage approaches prior to seeking a tuberculosis diagnosis. BMC Infect. Diseases 1–1 (2019)

    Google Scholar 

  15. S. Jaeger, A. Karargyris, S. Candemir, L. Folio, J. Siegelman, F. Callaghan, Z. Xue, K. Palaniappan, R.K. Singh, S. Antani, G. Thoma, Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging. (IEEE Press, 2013), pp. 233–45

    Google Scholar 

  16. N. Umar, Cost-effectiveness analysis of tuberculosis control strategies among migrants from Nigeria in the United Kingdom (Doctoral dissertation, University of East Anglia) (2015)

    Google Scholar 

  17. W. Rusdah, E. Edi, Review on data mining methods for tuberculosis diagnosis. Inform. Syst. 563–568 (2013)

    Google Scholar 

  18. N. Khan, ERP-communication framework: aerospace smart. Int. J. Comput. Sci. Inform. Secur. (2011)

    Google Scholar 

  19. J.B. Souza Filho, M. Sanchez, J.M. Seixas, C. Maidantchik, R. Galliez, A.D. Moreira, P.A. da Costa, M.M. Oliveira, A.D. Harries, A.L. Kritski, Screening for active pulmonary tuberculosis: development and applicability of artificial neural network models. Tuberculosis (Edinburgh, Scotland, 2018), pp. 94–101

    Google Scholar 

  20. F.E. Zulvia, R.J. Kuo, E. Roflin, An initial screening method for tuberculosis diseases using a multi-objective gradient evolution-based support vector machine and c5. 0 decision tree. in IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), (IEEE Press, 2017), pp. 204–209

    Google Scholar 

  21. V.I. Klassen, A.A. Safin, A.V. Maltsev, N.G. Andrianov, S.P. Morozov, A.V. Vladzymyrskyy, AI-based screening of pulmonary tuberculosis: diagnostic accuracy. J. eHealth Technol. Appl. 28–32 (2018)

    Google Scholar 

  22. R. Zachoval, P. Nencka, M. Vasakova, E. Kopecka, V. Borovička, J. Wallenfels, P. Cermak, The incidence of subclinical forms of urogenital tuberculosis in patients with pulmonary tuberculosis. J. Infect. Public Health 243–245 (2018)

    Google Scholar 

  23. K.S. Mithra, W.S. Emmanuel , GFNN: gaussian-Fuzzy-neural network for diagnosis of tuberculosis using sputum smear microscopic images. J. King Saud Univers.-Comput. Inform. Sci. 1084–95 (2021)

    Google Scholar 

  24. O. Stephen, M. Sain, U.J. Maduh, D.U. Jeong, An efficient deep learning approach to pneumonia classification in healthcare. J. Healthcare Eng. (2019)

    Google Scholar 

  25. K. Tomita, R. Nagao, H. Touge, T. Ikeuchi, H. Sano, A. Yamasaki, Y. Tohda, Deep learning facilitates the diagnosis of adult asthma. Allergology Int 456–461 (2019)

    Google Scholar 

  26. E.D. Alves, J.B. Souza Filho, A.L. Kritski, An ensemble approach for supporting the respiratory isolation of presumed tuberculosis inpatients. Neurocomputing 289–300 (2019)

    Google Scholar 

  27. A.D. Orjuela-Cañón, J.E. Mendoza, C.E. García, E.P. Vela, Tuberculosis diagnosis support analysis for precarious health information systems. Comput. Methods Programs Biomed. 11–17 (2018)

    Google Scholar 

  28. Y. Wu, H. Wang, F. Wu, Automatic classification of pulmonary tuberculosis and sarcoidosis based on random forest. in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). (IEEE Press, 2017), pp. 1–5

    Google Scholar 

  29. E. Rusdah, Winarko, R. Wardoyo, Predicting the suspect of new pulmonary tuberculosis case using SVM, C5. 0 and modified moran's I. Int. J. Comput. Sci. Netw. Secur. 164–71 (2017)

    Google Scholar 

  30. S. Benbelkacem, B. Atmani, Benamina, Treatment tuberculosis retrieval using decision tree. in 2013 International Conference on Control, Decision and İnformation Technologies (CoDIT). (IEEE Press, 2013), pp. 283–288

    Google Scholar 

  31. E.D. Alves, J.B. Souza Filho, R.M. Galliez, A. Kritski, Specialized MLP classifiers to support the isolation of patients suspected of pulmonary tuberculosis. in 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence. (IEEE Press, 2013), pp. 40–45

    Google Scholar 

  32. M.S. Hossain, F. Ahmed, K. Andersson, A belief rule based expert system to assess tuberculosis under uncertainty. J. Med. Syst. 1–11 (2017)

    Google Scholar 

  33. S. Sebhatu, A. Kumar, S. Pooja, Applications of soft computing techniques for pulmonary tuberculosis diagnosis. Int. J. Recent Technol. Eng. 1–9 (2019)

    Google Scholar 

  34. S. Kulkarni, S. Jha, Artificial intelligence, radiology, and tuberculosis: a review. Academic Radiol 71–75 (2020)

    Google Scholar 

  35. M.T. Khan, A.C. Kaushik, L. Ji, S.I. Malik, S. Ali, D.Q. Wei, Artificial neural networks for prediction of tuberculosis disease. Frontiers Microbiol 395–403 (2019)

    Google Scholar 

  36. A. Yahiaoui, O. Er, N. Yumuşak, A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines. Biomed. Res. 4208–4212 (2017)

    Google Scholar 

  37. N. Aini, H.R. Hatta, F. Agus, Z. Ariffin, Certain factor analysis for extrapulmonary tuberculosis diagnosis. in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (IEEE Press, 2017), pp. 1–7

    Google Scholar 

  38. B. Kaewseekhao, N. Nuntawong, P. Eiamchai, S. Roytrakul, W. Reechaipichitkul, K. Faksri, Diagnosis of active tuberculosis and latent tuberculosis infection based on Raman spectroscopy and surface-enhanced Raman spectroscopy. Tuberculosis 462–491 (2020)

    Google Scholar 

  39. T. Khatibi, A. Farahani, S.H. Armadian, Proposing a two-step decision support system (TPIS) based on stacked ensemble classifier for early and low cost (step-1) and final (step-2) differential diagnosis of Mycobacterium tuberculosis from non-tuberculosis Pneumonia. arXiv preprint (2020)

    Google Scholar 

  40. M. Beccaria, T.R. Mellors, J.S. Petion, C.A. Rees, M. Nasir, H.K. Systrom, J.W. Sairistil, M.A. Jean-Juste, V. Rivera, K. Lavoile, P. Severe, Preliminary investigation of human exhaled breath for tuberculosis diagnosis by multidimensional gas chromatography–time of flight mass spectrometry and machine learning. J. Chromatography 1074, 46–50 (2018)

    Google Scholar 

  41. M. Claesen, F. De Smet, J. Suykens, B. De Moor, EnsembleSVM: a library for ensemble learning using support vector machines. arXiv preprint (2014)

    Google Scholar 

  42. M. Syafrullah, Diagnosis of smear-negative pulmonary tuberculosis using ensemble method: a preliminary research. in 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). (IEEE Press, 2019), pp. 112–116

    Google Scholar 

  43. Z. Chen, H. Jiang, Y. Tan, T. Kudinha, J. Cui, L. Zheng, C. Cai, W. Li, C. Zhuo, Value of the Xpert MTB/RIF assay in diagnosis of presumptive pulmonary tuberculosis in general hospitals in China. Radiol. Infectious Diseases 147–53 (2020)

    Google Scholar 

  44. H.H. Rashidi, L.T. Dang, S. Albahra, R. Ravindran, I.H. Khan, Automated machine learning for endemic active tuberculosis prediction from multiplex serological data. Scientif. Rep. 1–12 (2021)

    Google Scholar 

  45. M.H. Lino Ferreira da Silva Barros, G. Oliveira Alves, L. Morais Florêncio Souza, E. da Silva Rocha, J.F. Lorenzato de Oliveira, T. Lynn, V. Sampaio, P.T. Endo, Benchmarking machine learning models to assist in the prognosis of tuberculosis. (Informatics, Multidisciplinary Digital Publishing Institute, 2021)

    Google Scholar 

  46. H.H. Rashidi, L.T. Dang, S. Albahra, R. Ravindran, I.H. Khan: Automated machine learning for endemic active tuberculosis prediction from multiplex serological data. Scientif. Reports 1–12 (2021)

    Google Scholar 

  47. S.P. Kailasam, Prediction of tuberculosis diagnosis using weighted KNN classifier. 502–509 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Sharda University, Plot No.32-34, KP III, Greater Noida, UP, 201310, India

    Sıraj Sebhatu,  Pooja & Parmd Nand

Authors
  1. Sıraj Sebhatu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pooja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parmd Nand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sıraj Sebhatu .

Editor information

Editors and Affiliations

  1. Department of EEE, Shree Venkateshwara Hi-Tech Engg College, Gobichettipalayam, India

    P. Karuppusamy

  2. Castilla-La Mancha University, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Tu N. Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sebhatu, S., Pooja, Nand, P. (2022). Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_41

Download citation

Publish with us

Policies and ethics

Search

Navigation