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 +/- 16.4 kg. 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.
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 obesepatients 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 obesepatients seeking surgical treatment. METHODOLOGY: 251 morbidly obesepatients 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 +/- 16.4 kg. 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.
Authors: Daniel Toman; Petr Vavra; Petr Jelinek; Petr Ostruszka; Peter Ihnat; Ales Foltys; Anton Pelikan; Jan Roman Journal: Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub Date: 2021-04-19 Impact factor: 1.245
Authors: Yingjun Quan; Ao Huang; Min Ye; Ming Xu; Biao Zhuang; Peng Zhang; Bo Yu; Zhijun Min Journal: Gastroenterol Res Pract Date: 2015-06-17 Impact factor: 2.260
Authors: Su Yon Jung; Mara Z Vitolins; Jenifer Fenton; Alexis C Frazier-Wood; Stephen D Hursting; Shine Chang Journal: PLoS One Date: 2015-03-30 Impact factor: 3.240
Authors: Marta Borges-Canha; João Sérgio Neves; Fernando Mendonça; Maria Manuel Silva; Cláudia Costa; Pedro M Cabral; Vanessa Guerreiro; Rita Lourenço; Patrícia Meira; Daniela Salazar; Maria João Ferreira; Jorge Pedro; Ebrahim Barkoudah; Ana Sande; Eva Lau; Selma B Souto; John Preto; Paula Freitas; Davide Carvalho Journal: Front Endocrinol (Lausanne) Date: 2021-08-12 Impact factor: 5.555