title: Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis.
abstract: It is unclear what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS). The current study compared self-report symptom data of patients with ME or CFS with those with MS. The self-report data is from the DePaul Symptom Questionnaire, and participants were recruited to take the questionnaire online. Data were analyzed using a machine learning technique called decision trees. Five symptoms best differentiated the groups. The best discriminating symptoms were from the immune domain (i.e., flu-like symptoms and tender lymph nodes), and the trees correctly categorized MS from ME or CFS 81.2% of the time, with those with ME or CFS having more severe symptoms. Our findings support the use of machine learning to further explore the unique nature of these different chronic diseases.
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Diana
authors.initials: D
authors.lastname: Ohanian
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Abigail
authors.initials: A
authors.lastname: Brown
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Madison
authors.initials: M
authors.lastname: Sunnquist
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Jacob
authors.initials: J
authors.lastname: Furst
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Nicholson
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Lauren
authors.initials: L
authors.lastname: Klebek
authors.affiliation: DePaul University, Chicago, USA.
authors.forename: Leonard A
authors.initials: LA
authors.lastname: Jason
keywords: Chronic Fatigue Syndrome; Data Mining; Decision Trees; Multiple Sclerosis; Myalgic Encephalomyelitis