Document Detail


A preclustering-based ensemble learning technique for acute appendicitis diagnoses.
MedLine Citation:
PMID:  23623208     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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. MATERIALS AND 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. CONCLUSION: 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:
Yen-Hsien Lee; Paul Jen-Hwa Hu; Tsang-Hsiang Cheng; Te-Chia Huang; Wei-Yao Chuang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-4-23
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  -     ISSN:  1873-2860     ISO Abbreviation:  Artif Intell Med     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-4-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2013 Elsevier B.V. All rights reserved.
Affiliation:
Department of Management Information Systems National Chiayi University, 580 Sinmin Road, Chiayi City 600, Taiwan.
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