Knowledge Discovery in Databases: Comorbidities in Tuberculosis Cases

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Carvalho, Isabelle ; Neiva, Mariane Barros ; Brandao Miyoshi, Newton Shydeo ; Crepaldi, Nathalia Yukie ; Bernardi, Filipe Andrade ; Lima, Vinicius Costa ; dos Santos, Ketlin Fabri ; de Andrade Mioto, Ana Clara ; Mozini, Mariana Tavares ; Galliez, Rafael Mello ; Sanchez, Mauro Niskier ; Kritski, Afranio Lineu ; Alves, Domingos ; Groen, D ; DeMulatier, C ; Paszynski, M ; Krzhizhanovskaya, VV ; Dongarra, JJ ; Sloot, PMA

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL SCIENCE - ICCS 2022, PT III; v. 13352, p. 11-pg., 2022-01-01.

Resumo

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 Sao 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. (AU)

• Processo FAPESP: 22/00020-0 - DSS-TB: sistema de suporte à decisão para Tuberculose
• Beneficiário: Mariana Tavares Mozini
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 20/01975-9 - Saúde digital para a estratégia End TB: da integração de dados ligados a uma melhor tomada de decisão baseada em evidências
• Beneficiário: Domingos Alves
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Regular