title: Predicting rotator cuff tears using data mining and Bayesian likelihood ratios.
abstract: OBJECTIVE
  Rotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone.
  
  
  METHODS
  In this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into "tear" and "no tear" groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models.
  
  
  RESULTS
  Our proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear).
  
  
  CONCLUSIONS
  Our predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears.
authors.affiliation: Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan.
authors.forename: Hsueh-Yi
authors.initials: HY
authors.lastname: Lu
authors.affiliation: Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan.
authors.forename: Chen-Yuan
authors.initials: CY
authors.lastname: Huang
authors.affiliation: Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Touliu, Yunlin, Taiwan.
authors.forename: Chwen-Tzeng
authors.initials: CT
authors.lastname: Su
authors.affiliation: Department of Orthopedics, National Taiwan University Hospital Yun-Lin Branch, Touliu, Yunlin, Taiwan.
authors.forename: Chen-Chiang
authors.initials: CC
authors.lastname: Lin
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D057225:Data Mining; D003710:Demography; D005260:Female; D006801:Humans; D007592:Joint Diseases / Q000175:diagnosis* / Q000473:pathology; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D012015:Reference Standards; D015203:Reproducibility of Results; D017006:Rotator Cuff / Q000473:pathology; D000070636:Rotator Cuff Injuries; D012421:Rupture; D055815:Young Adult
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