title: Generation of classification criteria for chronic fatigue syndrome using an artificial neural network and traditional criteria set.
abstract: OBJECTIVE
  The definition of chronic fatigue syndrome (CFS) is still disputed and no validated classification criteria have been published. Artificial neural networks (ANN) are computer-based models that can help to evaluate complex correlations. We examined the utility of ANN and other conventional methods in generating classification criteria for CFS compared to other diseases with prominent fatigue, systemic lupus erythematosus (SLE) and fibromyalgia syndrome (FMA).
  
  
  METHODS
  Ninety-nine case patients with CFS, 41 patients with SLE and 58 with FMA were recruited from a generalist outpatient population. Clinical symptoms were documented with help of a predefined questionnaire. The patients were randomly divided into two groups. One group (n = 158) served to derive classification criteria sets by two-fold cross-validation, using a) unweighted application of criteria, b) regression coefficients, c) regression tree analysis, and d) artificial neural networks in parallel. These criteria were validated with the second group (n = 40).
  
  
  RESULTS
  Classification criteria developed by ANN were found to have a sensitivity of 95% and a specificity of 85%. ANN achieved a higher accuracy than any of the other methods.
  
  
  CONCLUSIONS
  We present validated criteria for the classification of CFS versus SLE and FMA, comparing different classification approaches. The most accurate criteria were derived with the help of ANN. We therefore recommend the use of ANN for the classification of syndromes with complex interrelated symptoms like CFS.
authors: Institute of Medical Informatics, Medical University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany. linder@medinf.mu-luebeck.de
authors: R
authors: R
authors: Linder
authors: R
authors: R
authors: Dinser
authors: M
authors: M
authors: Wagner
authors: G R F
authors: GR
authors: Krueger
authors: A
authors: A
authors: Hoffmann
mesh_terms: D000328:Adult; D003937:Diagnosis, Differential; D015673:Fatigue Syndrome, Chronic / Q000145:classification / Q000175:diagnosis*; D005260:Female; D005356:Fibromyalgia / Q000145:classification / Q000175:diagnosis; D006801:Humans; D008180:Lupus Erythematosus, Systemic / Q000145:classification / Q000175:diagnosis; D008297:Male; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity