Detection of immunologically significant factors for chronic fatigue syndrome using neural-network classifiers

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.

FIG. 1

Clustering dendrogram of CFS (X) and control (O) cases on 29 immunologically defined features.

FIG. 2

Overall MSE over epochs.

FIG. 3

Sensitivity analysis for network classifier, showing relative contributions of each immunological factor. TNF. A, tumor necrosis factor alpha; IFN. G, gamma interferon; lymph. lymphocytes; P, percent; N, number.