Abstract: Detection of Immunologically Significant Factors for Chronic Fatigue Syndrome Using Neural-Network Classifiers

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Clin Diagn Lab Immunol 2001 May;8(3):658-62

Hanson SJ, Gause W, Natelson B. Department of Psychology, Rutgers University, Newark, New Jersey 07102, USA. Contact: jose@kreizler.rutgers.edu

Abstract:

Neural-network classifiers were used to detect immunological differences in

groups of chronic fatigue syndrome (CFS) patients that heretofore had not

shown significant differences from controls. In the past linear methods were

unable to detect differences between CFS groups and non-CFS control groups

in the nonveteran population.

An examination of the cluster structure for 29 immunological factors revealed a complex, nonlinear decision surface.

Multilayer neural networks showed an over 16% improvement in an n-fold

resampling generalization test on unseen data. A sensitivity analysis of the

network found differences between groups that are consistent with the

hypothesis that CFS symptoms are a consequence of immune system

dysregulation.

Corresponding decreases in the CD19(+) B-cell compartment and the CD34(+) hematopoietic progenitor subpopulation were also detected by the neural network, consistent with the T-cell expansion. Of significant interest was the fact that, of all the cytokines evaluated, the only one to be in the final model was interleukin-4 (IL-4). Seeing an increase in IL-4 suggests a shift to a type 2 cytokine pattern. Such a shift has been hypothesized, but until now convincing evidence to support that hypothesis has been lacking.

PMID: 11329477

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