Clinical and Diagnostic Laboratory Immunology
        658-662, Vol. 8, No. 3, pp 658-662

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

S.J. Hanson (1,*), W. Gause (2), and B. Natelson (3)
Rutgers University(1) and UMDNJ-New Jersey Medical School (3), Newark, New
Jersey, and Uniformed Services of the Health Sciences, Bethesda, Maryland (2)

* Corresponding author. Mailing address: Department of Psychology, Smith Hall,
  Rutgers University, Newark, NJ 07102. Phone: (973) 353-5440, ext. 5095. Fax:
  (973) 353-1171. E-mail: jose@kreizler.rutgers.edu.


Received 24 May 2000/Returned for modification 25 July 2000/Accepted 5
February 2001

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.

(c) 2001 American Society for Microbiology