title: A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software.
abstract: BACKGROUND
  Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses.
  
  
  METHODS
  Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes.
  
  
  RESULTS
  Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree.
  
  
  CONCLUSIONS
  BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources.
authors.affiliation: Department of Inflammatory Bowel Disease, Research Center for Gastroenterology and Liver Diseases, Shaheed Beheshti University of Medical Sciences, Tehran, Iran. firouzifar1975@yahoo.com
authors.forename: Farzad
authors.initials: F
authors.lastname: Firouzi
authors.forename: Marjan
authors.initials: M
authors.lastname: Rashidi
authors.forename: Sattar
authors.initials: S
authors.lastname: Hashemi
authors.forename: Mohammadreza
authors.initials: M
authors.lastname: Kangavari
authors.forename: Ali
authors.initials: A
authors.lastname: Bahari
authors.forename: Naser Ebrahimi
authors.initials: NE
authors.lastname: Daryani
authors.forename: Mohammad Mehdi
authors.initials: MM
authors.lastname: Emam
authors.forename: Nosratollah
authors.initials: N
authors.lastname: Naderi
authors.forename: Hamid Mohaghegh
authors.initials: HM
authors.lastname: Shalmani
authors.forename: Alma
authors.initials: A
authors.lastname: Farnood
authors.forename: Mohammadreza
authors.initials: M
authors.lastname: Zali
mesh_terms: D015502:Absorptiometry, Photon; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D015519:Bone Density; D003093:Colitis, Ulcerative / Q000150:complications / Q000503:physiopathology; D003424:Crohn Disease / Q000150:complications / Q000503:physiopathology; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000150:complications* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D010024:Osteoporosis / Q000175:diagnosis* / Q000209:etiology*; D018579:Patient Selection; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects / Q000503:physiopathology; D012984:Software