Generation of classification criteria for chronic fatigue syndrome using an artificial neural network and traditional criteria set

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). Patients and 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. Conclusion: 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.