MIDL 2021

International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

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

TitleGeneration of classification criteria for chronic fatigue syndrome using an artificial neural network and traditional criteria set.
Publication TypeJournal Article
Year of Publication2002
AuthorsLinder R., Dinser R., Wagner M., Krueger G.R.F., Hoffmann A.
JournalIn vivo (Athens, Greece)
Date Published2002 Jan-Feb
Publication Languageeng
KeywordsAdult, Diagnosis, Differential, Fatigue Syndrome, Chronic, Female, Fibromyalgia, Humans, Lupus Erythematosus, Systemic, Male, Neural Networks (Computer), Sensitivity and Specificity

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.

PubMed Link


Alternate JournalIn Vivo
Created at November 9, 2012 - 4:06pm by Kulbe.


Program of Study

Study Medical Informatics
at the University of Lübeck

read more ...


Susanne Petersen

Tel+49 451 3101 5601
Fax+49 451 3101 5604

Ratzeburger Allee 160
23538 Lübeck