JMIR medical informatics · 2026
Multioutput Machine Learning Model for Predicting Postoperative Outcomes After Liposuction: Algorithm Development and Validation Study in a Multicenter Cohort.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Lee Chaewoo, Park Seoyoung, Hwang Jiyoung, Woo Selin, Park Youn Chan, Seo Jae Won, Lee Sun Ho, Ahn Jae Hyun, Yon Dong Keon, Rhee Sang Youl
Аннотация
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 multioutput 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 8 individual ML models, an automated ML approach, 2 ensemble approaches, and chained multioutput regression models for predicting postoperative body weight and circumferential size. Models were developed using 5-fold cross-validation and evaluated on an independent test set. Performance was assessed using the coefficient of determination (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 selected model was integrated into a web-based clinical decision support system (CDSS).
RESULTS: A total of 7804 individuals who underwent liposuction were included; of these, 7612 (97.54%) were female. The chained extra trees regressor model with a weight-to-size prediction order achieved an R2 of 0.98, an RMSE of 2.36, an MAE of 1.24, and a MAPE of 2.19. The SHAP analysis identified preoperative weight as the main predictor of postoperative body weight and preoperative size and liposuction-related factors as key predictors of postoperative circumferential size. The final model was integrated into a web-based CDSS (365mc AI platform).
CONCLUSIONS: We developed and validated a chained multioutput 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.
OBJECTIVE: This study aimed to develop and validate a chained multioutput 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 8 individual ML models, an automated ML approach, 2 ensemble approaches, and chained multioutput regression models for predicting postoperative body weight and circumferential size. Models were developed using 5-fold cross-validation and evaluated on an independent test set. Performance was assessed using the coefficient of determination (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 selected model was integrated into a web-based clinical decision support system (CDSS).
RESULTS: A total of 7804 individuals who underwent liposuction were included; of these, 7612 (97.54%) were female. The chained extra trees regressor model with a weight-to-size prediction order achieved an R2 of 0.98, an RMSE of 2.36, an MAE of 1.24, and a MAPE of 2.19. The SHAP analysis identified preoperative weight as the main predictor of postoperative body weight and preoperative size and liposuction-related factors as key predictors of postoperative circumferential size. The final model was integrated into a web-based CDSS (365mc AI platform).
CONCLUSIONS: We developed and validated a chained multioutput 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.