Predicting Weight Loss Success After Gastric Sleeve Surgery: A Machine Learning-Based Approach

Obesity is a global health issue, and in this context, bariatric surgery is considered the most effective treatment for severe cases. However, postoperative outcomes vary widely among individuals, driving the development of tools to predict body weight loss success. The main objective of this paper is to evaluate predictive variables for successful weight loss one year after Sleeve bariatric surgery, defining success as a weight loss exceeding 30%.

A dataset of 94 cases was included in this study. Data were collected between 2013 and 2018 from the Nutrition Section of the Endocrinology and Nutrition Department in the Eastern Area of Valladolid, Spain. Machine learning algorithms applied included Random Forest, Multilayer Perceptron, XGBoost, Decision Tree, Logistic Regression, and Support Vector Machines (SVMs).

The SVM model demonstrated the best performance, attaining an accuracy of 88% and an area under the curve (AUC) of 0.76 with a 95% CI between 0.5238 and 0.9658. The main predictive variables identified were potassium (K), folic acid, alkaline phosphatase (ALP), height, transferrin, weight, body mass index (BMI), triglyceride (Tg), Beck Depression Test score, and insulin levels.

In conclusion, this study highlights the potential of machine learning models, particularly Support Vector Machines (SVMs), in predicting successful weight loss after Sleeve bariatric surgery. The key predictive variables identified include biochemical markers, anthropometric measures, and psychological factors, emphasizing the multifactorial nature of postoperative weight loss outcomes.