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Logo of bmcmidmBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Medical Informatics and Decision Making
 
BMC Med Inform Decis Mak. 2012; 12: 17.
Published online Mar 13, 2012. doi:  10.1186/1472-6947-12-17
PMCID: PMC3314559
A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis
Chang Sik Son,1 Byoung Kuk Jang,2 Suk Tae Seo,3 Min Soo Kim,3 and Yoon Nyun Kimcorresponding author1,2,3
1Department of Medical Informatics, School of Medicine, Keimyung University, 2800 Dalgubeoldaero, Dalseo-Gu, Daegu, Republic of Korea
2Department of Internal Medicine, School of Medicine, Keimyung University, 2800 Dalgubeoldaero, Dalseo-Gu, Daegu, Republic of Korea
3Biomedical Information Technology Center, School of Medicine, Keimyung University, 2800 Dalgubeoldaero, Dalseo-Gu, Daegu, Republic of Korea
corresponding authorCorresponding author.
Chang Sik Son: csson/at/kmu.ac.kr; Byoung Kuk Jang: jangha106/at/dsmc.or.kr; Suk Tae Seo: kenneth78/at/kmu.ac.kr; Min Soo Kim: msk0328/at/kmu.ac.kr; Yoon Nyun Kim: ynkim/at/dsmc.or.kr
Received September 19, 2011; Accepted March 13, 2012.
Abstract
Background
The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules.
Methods
We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing.
Results
Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data.
Conclusions
The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis.
Keywords: Hybrid decision support model, Acute appendicitis, Knowledge discovery, Decision tree, Logistic regression analysis
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