Abstract
Unlike the primary condition under investigation, the term comorbidities define coexisting medical conditions that influence patient care during detection, therapy, and outcome. Tuberculosis continues to be one of the 10 leading causes of death globally. The aim of the study is to present the exploration of classic data mining techniques to find relationships between the outcome of TB cases (cure or death) and the comorbidities presented by the patient. The data are provided by TBWEB and represent TB cases in the territory of the state of São Paulo-Brazil, from 2006 to 2016. Techniques of feature selection and classification models were explored. As shown in the results, it was found high relevance for AIDS and alcoholism as comorbidities in the outcome of TB cases. Although the classifier performance did not present a significant statistical difference, there was a great reduction in the number of attributes and in the number of rules generated, showing, even more, the high relevance of the attributes: age group, AIDS, and other immunology in the classification of the outcome of TB cases. The explored techniques proved to be promising to support searching for unclear relationships in the TB context, providing, on average, a 73% accuracy in predicting the outcome of the cases according to characteristics that were analyzed.
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Acknowledgements
We thank Freepik (www.freepik.com) to provide the icons used in the composition of Fig. 1. DA would like to thank the São Paulo Research Foundation for financial support (Process numbers: 2022/00020-0 \(\mid \) 2021/01961 \(\mid \) 2020/01975-9).
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Carvalho, I. et al. (2022). Knowledge Discovery in Databases: Comorbidities in Tuberculosis Cases. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_1
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