JMIR Publications

ABSTRACT

Background:

Liposuction is widely performed to remove localized fat deposits and improve body contour, yet individualized prediction of postoperative outcomes remains challenging. Existing machine learning (ML) studies have largely focused on single-outcome prediction, with limited attention to the interdependence between postoperative body weight and circumferential size.

Objective:

This study aimed to develop and validate a chained multi-output ML framework to jointly predict postoperative body weight and circumferential size after liposuction using a large multicenter cohort from the 365MC network.

Methods:

We analyzed a multicenter cohort of 7804 individuals who underwent liposuction in 2024 at 20 obesity specialty clinics in the 365MC network across South Korea. Using 15 predictors, we compared eight individual ML models, an automated ML approach, two ensemble approaches, and chained multi-output regression models for predicting postoperative body weight and circumferential size. Models were developed using five-fold cross-validation and evaluated on an independent test set. Performance was assessed using R², root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and feature importance was evaluated using Shapley Additive Explanation (SHAP) values. The best-performing model was integrated into a web-based clinical decision support system (CDSS).

Results:

A total of 7804 individuals who underwent liposuction (7612 [97.54%] females) were included. The chained Extra Trees Regressor model with a weight-to-size prediction order showed the best performance, achieving an R² of 0.98, RMSE of 2.36, MAE of 1.24, and MAPE of 2.19. SHAP analysis identified preoperative weight as the main predictor of postoperative body weight and preoperative size with liposuction-related factors as key predictors of postoperative circumferential size. The final model was integrated into a web-based CDSS accessible at https://365mc.vercel.app.

Conclusions:

We developed and validated a chained multi-output regression model to predict postoperative body weight and circumferential size after liposuction. Integrated into a web-based CDSS, the model may support patient-specific preoperative counseling and surgical planning.