Prediction of successful weight reduction after bariatric surgery by data mining technologies.
BACKGROUND
Surgery is the only long-lasting effective treatment for morbid obesity. Prediction on successful weight loss after surgery by data mining technologies is lacking. We analyze the available information during the initial evaluation of patients referred to bariatric surgery by data mining methods for predictors of successful weight loss.


METHODS
249 patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) were enrolled. Logistic Regression and Artificial Neural Network (ANN) technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs.


RESULTS
We studied 249 patients consisting of 72 men and 177 women over 2 years. Mean age was 33 +/- 9 years. 208 (83.5%) patients had successful weight reduction while 41 (16.5%) did not. Logistic Regression revealed that the type of operation had a significant prediction effect (P = 0.000). Patients receiving LMGB had a better weight loss than those receiving LAGB (78.54% +/- 26.87 vs 43.65% +/- 26.08). ANN provided the same predicted factor on the type of operation but it further proposed that HbAlc and triglyceride were associated with success. HbAlc is lower in the successful than failed group (5.81 +/- 1.06 vs 6.05 +/- 1.49; P = NS), and triglyceride in the successful group is higher than in the failed group (171.29 +/- 112.62 vs 144.07 +/- 89.90; P = NS).


CONCLUSIONS
Artificial neural network is a better modeling technique and the overall predictive accuracy is higher on the basis of multiple variables related to laboratory tests. LMGB, high preoperative triglyceride level, and low HbAlc level can predict successful weight reduction at 2 years.
Lee
Yi-Chih
YC
Graduate Institute of Business Administration, Fu-Jen Catholic University, Taipei Hsien, Taiwan.
Lee
Wei-Jei
WJ
Lee
Tian-Shyug
TS
Lin
Yang-Chu
YC
Wang
Weu
W
Liew
Phui-Ly
PL
Huang
Ming-Te
MT
Chien
Ching-Wen
CW
D000328:Adult; D050110:Bariatric Surgery; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011446:Prospective Studies; D016896:Treatment Outcome; D015431:Weight Loss