title: Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition.
abstract: This article contrasts two case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We compared the empiric CFS case definition (Reeves et al., 2005) and the Canadian ME/CFS clinical case definition (Carruthers et al., 2003) with a sample of individuals with CFS versus those without. Data mining with decision trees was used to identify the best items to identify patients with CFS. Data mining is a statistical technique that was used to help determine which of the survey questions were most effective for accurately classifying cases. The empiric criteria identified about 79% of patients with CFS and the Canadian criteria identified 87% of patients. Items identified by the Canadian criteria had more construct validity. The implications of these findings are discussed.
authors.affiliation: DePaul University, USA. Ljason@depaul.edu
authors.forename: Leonard A
authors.initials: LA
authors.lastname: Jason
authors.forename: Beth
authors.initials: B
authors.lastname: Skendrovic
authors.forename: Jacob
authors.initials: J
authors.lastname: Furst
authors.forename: Abigail
authors.initials: A
authors.lastname: Brown
authors.forename: Angela
authors.initials: A
authors.lastname: Weng
authors.forename: Christine
authors.initials: C
authors.lastname: Bronikowski
mesh_terms: D002170:Canada; D002641:Chicago; D002908:Chronic Disease; D057225:Data Mining / Q000379:methods*; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005221:Fatigue / Q000175:diagnosis*; D015673:Fatigue Syndrome, Chronic / Q000175:diagnosis*; D005500:Follow-Up Studies; D006306:Health Surveys; D006801:Humans; D007406:Interview, Psychological; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics / Q000295:instrumentation*; D015203:Reproducibility of Results; D012306:Risk; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index