Model for predicting drug resistance based on the clinical profile of tuberculosis patients using machine learning techniques

被引:0
作者
Falcao, Igor Wenner Silva [1 ]
Cardoso, Diego Lisboa [1 ]
dos Santos Santos, Albert Einstein Coutinho [1 ]
Paixao, Erminio [1 ]
Costa, Fernando Augusto R. [2 ]
Figueiredo, Karla [3 ]
Carneiro, Saul [4 ]
da Rocha Seruffo, Marcos César [1 ]
机构
[1] Institute of Technology, Federal University of Para, PA, Belém
[2] Center for Higher Amazon Studies, Federal University of Para, PA, Belém
[3] Computer Science, State University of Rio de Janeiro, RJ, Rio de Janeiro
[4] João de Barros Barreto University Hospital, Federal University of Para, PA, Belém
关键词
Anti-tuberculosis; Drug resistance; Machine learning; Tuberculosis;
D O I
10.7717/PEERJ-CS.2246
中图分类号
学科分类号
摘要
Tuberculosis (TB) is a disease caused by the bacterium Mycobacterium tuberculosis and despite effective treatments, still affects millions of people worldwide. The advent of new treatments has not eliminated the significant challenge of TB drug resistance. Repeated and inadequate exposure to drugs has led to the development of strains of the bacteria that are resistant to conventional treatments, making the eradication of the disease even more complex. In this context, it is essential to seek more effective approaches to fighting TB. This article proposes a model for predicting drug resistance based on the clinical profile of TB patients, using machine learning techniques. The model aims to optimize the work of health professionals directly involved with tuberculosis patients, driving the creation of new containment strategies and preventive measures, as it specifies the clinical data that has the greatest impact and identifies the individuals with the greatest predisposition to develop resistance to anti-tuberculosis drugs. The results obtained show, in one of the scenarios, a probability of development of 70% and an accuracy of 84.65% for predicting drug resistance. © 2024 Falcão et al.
引用
收藏
相关论文
共 33 条
  • [1] Amiri MRJ, Siami R, Khaledi A., Tuberculosis status and coinfection of pulmonary fungal infections in patients referred to reference laboratory of health centers ghaemshahr city during 2007–2017, Ethiopian Journal of Health Sciences, 28, 6, pp. 683-690, (2018)
  • [2] Ashna H, Kaffash A, Khaledi A, Ghazvini, Mutations of rpob gene associated with rifampin resistance among mycobacterium tuberculosis isolated in tuberculosis regional reference laboratory in northeast of Iran during 2015–2016, Ethiopian Journal of Health Sciences, 28, 3, pp. 299-304, (2018)
  • [3] Biagioni BT, Cavicchioli M, Massabni AC., Silver complexes for tuberculosis treatment: a short review, Química Nova, 45, pp. 83-88, (2022)
  • [4] BRASIL A., Bactérias de tuberculose resistentes a antibióticos desafiam, (2018)
  • [5] Cao F, Liang J, Li D, Bai L, Dang C., A dissimilarity measure for the k-modes clustering algorithm, Knowledge-Based Systems, 26, 5, pp. 120-127, (2012)
  • [6] Chakaya J, Khan M, Ntoumi F, Aklillu E, Fatima R, Mwaba P, Kapata N, Mfinanga S, Hasnain SE, Katoto PD., Global tuberculosis report 2020–reflections on the global tb burden, treatment and prevention efforts, International Journal of Infectious Diseases, 113, 7, pp. S7-S12, (2021)
  • [7] Chakaya J, Petersen E, Nantanda R, Mungai BN, Migliori GB, Amanullah F, Lungu P, Ntoumi F, Kumarasamy N, Maeurer M., The who global tuberculosis 2021 report–not so good news and turning the tide back to end tb, International Journal of Infectious Diseases, 124, 1, pp. S26-S29, (2022)
  • [8] Cheepsattayakorn A., Drug-resistant tuberculosis–diagnosis, treatment, management and control: The experience in Thailand, Tuberculosis-Current Issues in Diagnosis and Management, (2013)
  • [9] Crepaldi NY, Lima VC, Bernardi FA, Santos LRA, Yamaguti VH, Pellison FC, Sanches TLM, Miyoshi NSB, Ruffino-Netto A, Rijo RPCL., Sistb: an ecosystem for monitoring tb, Procedia Computer Science, 164, pp. 587-594, (2019)
  • [10] Das S, Pradhan SK, Mishra S, Patra N, Pradhan S., Classification of pulmonary tuberculosis using mathematical modeling and machine learning, 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), pp. 176-182, (2022)