title: A screening system for smear-negative pulmonary tuberculosis using artificial neural networks.
abstract: OBJECTIVE
  Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed.
  
  
  METHODS
  The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital.
  
  
  RESULTS
  MLP showed higher sensitivity (100%, 95% confidence interval (CI) 78-100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60-96%), multivariate logistic regression (MLR) (79%; 95% CI 53-93%), and classification and regression tree (CART) (71%; 95% CI 45-88%). MLR showed a slightly higher specificity (85%; 95% CI 59-96%) than MLP (80%; 95% CI 54-93%), SVM linear (75%, 95% CI 49-90%), and CART (65%; 95% CI 39-84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824-1.000) than the SVM linear (0.796, 95% CI 0.651-0.970) and MLR (0.782, 95% CI 0.663-0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice.
  
  
  CONCLUSIONS
  In settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients.
authors.affiliation: Polytechnical School (POLI), Electronics and Computer Engineering Department (DEL), Avenida Athos da Silveira Ramos, 149, Technological Center, Building H, room H-219 (room 20), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Electrical Engineering Postgraduate Program (PPEEL), Federal Centre of Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil. Electronic address: jbfilho@poli.ufrj.br.
authors.forename: João B
authors.initials: JB
authors.lastname: de O Souza Filho
authors.affiliation: Polytechnical School (POLI), Electronics and Computer Engineering Department (DEL), Avenida Athos da Silveira Ramos, 149, Technological Center, Building H, room H-219 (room 20), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil; Electrical Engineering Postgraduate Program (PEE), Alberto Luiz Coimbra Institute (COPPE), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: José Manoel
authors.initials: JM
authors.lastname: de Seixas
authors.affiliation: Academic Tuberculosis Program, Faculty of Medicine and University Complex (HUFF and IDT), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Rafael
authors.initials: R
authors.lastname: Galliez
authors.affiliation: Academic Tuberculosis Program, Faculty of Medicine and University Complex (HUFF and IDT), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Basilio
authors.initials: B
authors.lastname: de Bragança Pereira
authors.affiliation: Academic Tuberculosis Program, Faculty of Medicine and University Complex (HUFF and IDT), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Fernanda C
authors.initials: FC
authors.lastname: de Q Mello
authors.affiliation: Postgraduate Program on Collective Health, Federal University of Maranhão, Maranhão, Brazil.
authors.forename: Alcione Miranda
authors.initials: AM
authors.lastname: Dos Santos
authors.affiliation: Academic Tuberculosis Program, Faculty of Medicine and University Complex (HUFF and IDT), Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Afranio Lineu
authors.initials: AL
authors.lastname: Kritski
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000295:instrumentation / Q000379:methods*; D008875:Middle Aged; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification / Q000502:physiology; D016571:Neural Networks, Computer; D012372:ROC Curve; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000382:microbiology
keywords: Computational intelligence; Data mining; Decision support systems