Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry

Abstract

Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.

Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

“…Many methods have been applied to predict the outcomes in patients after bariatric surgery, including stepwise multivariable linear regression [ 44 , 45 ], MLR [ 46 ], and machine learning methods such as the decision tree [ 47 ] and CNN [ 15 , 33 ]. Although an intelligent decision-making support system involving Bayesian networks has been reported for the nutrition diagnosis of bariatric surgery patients [ 48 ], according to our literature search, there is no study that has used the method for predicting outcomes after bariatric surgery.…”

“…Binary predictors were transformed into continuous propensity scores using MLR before they entered the GBN [29]. Performance factors of the GBN were all compared with those from the previous CNN [15] and a traditional linear regression model. Prediction for 5-year comorbidities was conducted using both multinomial discrete BN (DBN) and MLR, and the results from the two methods were compared.…”

“…Binary predictors were transformed into continuous propensity scores using MLR before they entered the GBN [ 29 ]. Performance factors of the GBN were all compared with those from the previous CNN [ 15 ] and a traditional linear regression model.…”

“…However, none of these studies evaluated the models' performances or the predictors' predictive abilities rigorously using real world data. In our previous study, we have examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg) [15]. We found that, although the CNN is better than the traditional multivariate linear regression model at predicting long-term HRQoL after bariatric surgery, the overfitting issue is still apparent and needs to be mitigated [15].…”

“…In our previous study, we have examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg) [15]. We found that, although the CNN is better than the traditional multivariate linear regression model at predicting long-term HRQoL after bariatric surgery, the overfitting issue is still apparent and needs to be mitigated [15]. In the two recently published studies, using the same database, we found that patients with postoperative complications had significantly less improvements in all aspects of HRQoL compared to those without any form of postoperative complication [16], and the ability of multilayer perceptron and CNN for predicting the postoperative serious complications after bariatric surgery is limited [17].…”