Obesity and the decision tree: predictors of sustained weight loss after bariatric surgery

Yi Chih Lee, Wei Jei Lee, Yang Chu Lin, Phui Ly Liew, Chia Ko Lee, Steven C.H. Lin, Tian Shyung Lee

研究成果: 雜誌貢獻文章同行評審

20 引文 斯高帕斯(Scopus)


Background/Aims: Bariatric surgery is the only long-lasting effective treatment to reduce body weight in morbid obesity. Previous literature in using data mining techniques to predict weight loss in obese patients who have undergone bariatric surgery is limited. This study used initial evaluations before bariatric surgery and data mining techniques to predict weight outcomes in morbidly obese patients seeking surgical treatment. Methodology: 251 morbidly obese patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) with complete clinical data at baseline and at two years were enrolled for analysis. Decision Tree, Logistic Regression and Discriminant analysis technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. Results: Two hundred fifty-one patients consisting of 68 men and 183 women was studied; with mean age 33 years. Mean ± SD weight loss at 2 year was 74.5±16.4kg. During two years of follow up, two-hundred and five (81.7%) patients had successful weight reduction while 46 (18.3%) were failed to reduce body weight. Operation methods, alanine transaminase (ALT), aspartate transaminase (AST), white blood cell counts (WBC), insulin and hemoglobin A1c (HbA1c) levels were the predictive factors for successful weight reduction. Conclusion: Decision tree model was a better classification models than traditional logistic regression and discriminant analysis in view of predictive accuracies.
頁(從 - 到)1745-1749
出版狀態已發佈 - 11月 2009

ASJC Scopus subject areas

  • 肝病
  • 消化內科


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