title: A preclustering-based ensemble learning technique for acute appendicitis diagnoses.
abstract: OBJECTIVE
  Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis.
  
  
  METHODS
  The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks.
  
  
  RESULTS
  The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619.
  
  
  CONCLUSIONS
  The PEL technique is capable of addressing imbalanced sample learning associated with acute appendicitis diagnosis. Our evaluation results suggest PEL is less biased toward a positive or negative class than the investigated benchmark techniques. In addition, our results indicate the overall effectiveness of the proposed technique, compared with prevalent scoring systems or salient classification techniques that follow the resampling approach.
authors.affiliation: Department of Management Information Systems National Chiayi University, 580 Sinmin Road, Chiayi City 600, Taiwan.
authors.forename: Yen-Hsien
authors.initials: YH
authors.lastname: Lee
authors.forename: Paul Jen-Hwa
authors.initials: PJ
authors.lastname: Hu
authors.forename: Tsang-Hsiang
authors.initials: TH
authors.lastname: Cheng
authors.forename: Te-Chia
authors.initials: TC
authors.lastname: Huang
authors.forename: Wei-Yao
authors.initials: WY
authors.lastname: Chuang
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D001064:Appendicitis / Q000145:classification / Q000175:diagnosis*; D019540:Area Under Curve; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003951:Diagnostic Errors / Q000517:prevention & control; D006801:Humans; D011237:Predictive Value of Tests; D012372:ROC Curve