Abstract
Tuberculosis (TB) is a chronic infectious disease and remains a serious explanation for death globally. Most of the infected people are from poverty-stricken communities with low healthcare infrastructure. Machine learning (ML) shows a unique way to facilitate the diagnosis of TB. ML can provide deep insights into large biomedical datasets, as well as it can uncover new biomedical and healthcare knowledge. ML techniques have to be utilized to successfully recognize TB occurrence at an early stage or re-occurrence. Association rule mining and decision tree can be used for prediction of the occurrence and re-occurrence of TB. Association rules and decision tree give a meaningful and efficient way to define and present certain dependencies among the attributes in a dataset ML algorithms help to identify, discover hidden and valuable information, and have been massively used and explored in the medical domain. The main purpose of this study is to apply ML techniques for extracting hidden patterns, which are significant to predict TB at an early stage.
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References
WHO Homepage, https://www.who.int/news-room/fact-sheets/detail/TB. Last accessed 21 Feb 2019
Zulvia F, Kuo R, Roflin E (2017) An initial screening method for TB diseases using a multi-objective gradient evolution based support vector machine and C5.0 DECISIONTREE. In: IEEE 41st annual computer software and applications conference (COMPSAC), pp 204–209. https://doi.org/10.1109/compsac.2017.57
Wu Y, Wang H, Wu F (2017) Automatic classification of pulmonary TB and sarcoidosis based on random forest. In: 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–5. https://doi.org/10.1109/cisp-bmei.2017.8302280
Subiyanto, Mulwinda A, Andriani D (2017) Intelligent diagnosis system for acute respiratory infection in infants. In: 3rd international conference on science in in-formation technology (ICSITech), pp 558–562. https://doi.org/10.1109/icsitech.2017. 825717
Dominic V, Aggarwal A, Gupta D, Khare S (2015) Investigation of chronic disease correlation using data mining techniques. In: 2nd international conference on recent advances in engineering and computational sciences (RAECS), pp 1–6. https://doi.org/10.1109/raecs.2015.7453329
Rusdah, Winarko E, Wardoyo R (2015) Preliminary diagnosis of pulmonary TB using ensemble method. In: 2015 international conference on data and software engineering (ICoDSE), pp 175–180. https://doi.org/10.1109/icodse.2015.7436993
Shukla M, Agarwal S (2014) Hybrid approach for TB data classification using optimal centroid selection based clustering. In: 2014 students conference on engineering and systems, pp 1–5. https://doi.org/10.1109/sces.2014.6880115
Benbelkacem S, Atmani B, Benamina M (2013) Treatment TB retrieval using decision tree. In: 2013 international conference on control, decision and information technologies (CoDIT), pp 283–288. https://doi.org/10.1109/codit.2013.6689558
Karthik D, Vijayarekha K, Dhivya V (2015) Identifying new regions infected by mycobacterium TB(TB): case study in Kumbakonam region, ThanjavurDistrict. Int J ChemTech Res CODEN (USA), pp 341–345(2015). IJCRGG ISSN: 0974-4290
Rusdah, Winarko E (2013) Review on data mining methods for TB diagnosis. In: Information systems international conference
Garg S, Rupal N (2015) A review on TB using data mining approaches. Int J Eng Dev Res. ISSN: 2321-9939
Asha T, Natarajan S, Murthy K (2012) Data mining techniques in the diagnosis of TB. www.intechopen.com. https://doi.org/10.5772/30504
Gupta D, Aggarwal A, Khare S (2016) A method to predict diagnostic codes for chronic diseases using ML techniques. In: Fifth IEEE international conference on computing communication and automation (ICCA), pp. 281–287. https://doi.org/10.1109/ccaa.2016.7813730
Khare S, Gupta D (2016) Association rule analysis in cardiovascular disease. In: Second international conference on cognitive computing and information processing (CCIP), SJCE, Mysuru, India, pp. 1–6. IEEE. https://doi.org/10.1109/ccip.2016. 7802881
Han J, Kamber M, Pei J (2017) Data mining: concepts and techniques, 3rd edn. Elsevier Science & Technology, US The Morgan sKaufmann Series
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Mohapatra, A., Khare, S., Gupta, D. (2021). Analysis of Tuberculosis Disease Using Association Rule Mining. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_74
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DOI: https://doi.org/10.1007/978-981-15-3514-7_74
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