Prediction of successful weight reduction after bariatric surgery by data mining technologies

Abstract

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.

Predictive factors of excess body weight loss 1 year after laparoscopic bariatric surgery

“…Excess body weight loss 1 year after surgery (74%) was roughly similar to that of other published series using similar surgical techniques. Age and BMI were negative predictors of weight loss in many, but not all [12,26], previously reported BS studies [27][28][29][30][31][32][33][34]. EWL has been reported to be greater in females [17,27,29,34], greater in males [29,32], or no gender differences have been shown [26,28,31,33].…”

“…We are aware of only one study that evaluated the association between baseline HbA1c and TG with EWL after BS [26]. This study, in which diabetic status was not reported, showed that people in the successful group had lower baseline HbA1c but higher TG concentrations [26]. Some limitations of our study should be acknowledged.…”

“…Therefore, we could consider HbA1c and TG, which are both higher in diabetic than in nondiabetic individuals and associated with insulin resistance, as indirect markers of insulin sensitivity. We are aware of only one study that evaluated the association between baseline HbA1c and TG with EWL after BS [26]. This study, in which diabetic status was not reported, showed that people in the successful group had lower baseline HbA1c but higher TG concentrations [26].…”

Predictive Factors of Weight Loss 1 Year after Laparoscopic Gastric Bypass in Obese Patients

“…Some predictive factors of poor weight loss after gastric banding surgery have been identified in previous studies [7][8][9][10]. Similarly, after gastric bypass, several preoperative predictors of poor weight loss have already been identified such as initial weight or body mass index [6,[11][12][13][14], advanced age [13,15], and female gender [13,16].…”

The comparative analysis of various classification models on land evaluation

“…Table 4 expresses test index of established logistic model. The model had a significant effect on weight reduction (Sig=0.000) by regression analysis [17] .…”