Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea
Abstract This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study used longitudinal cohort data from 2018 to 2023 from five nationwide centers affiliated with 365MC Liposuction Hospital, the largest liposucti...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79654-y |
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| author | Hyunji Sang Jaeyu Park Soeun Kim Myeongcheol Lee Hojae Lee Sun-Ho Lee Dong Keon Yon Sang Youl Rhee |
| author_facet | Hyunji Sang Jaeyu Park Soeun Kim Myeongcheol Lee Hojae Lee Sun-Ho Lee Dong Keon Yon Sang Youl Rhee |
| author_sort | Hyunji Sang |
| collection | DOAJ |
| description | Abstract This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study used longitudinal cohort data from 2018 to 2023 from five nationwide centers affiliated with 365MC Liposuction Hospital, the largest liposuction hospitals in Korea. Fifteen variables related to patient profiles were integrated and applied to various ML algorithms, including random forest, support vector, XGBoost, decision tree, and AdaBoost regressors. Performance evaluation employed mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) score. Feature importance and RMSE importance analyses were performed to compare the influence of each feature on prediction performance. A total of 9,856 were included in the final analysis. The random forest regressor model best predicted the liposuction volume (MAE, 0.197, RMSE, 0.249, R2, 0.792). Body fat mass and waist circumference were the most important features of the random forest regressor model (feature importance 71.55 and 13.21, RMSE importance 0.201 and 0.221, respectively). Leveraging this model, a web-based application was developed to suggest ideal liposuction volumes. These findings could be used in clinical practice to enhance decision-making and tailor surgical interventions to individual patient needs, thereby improving overall surgical efficacy and patient satisfaction. |
| format | Article |
| id | doaj-art-11e6b034d3ac489d9159e6c1d42292f7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-11e6b034d3ac489d9159e6c1d42292f72025-01-26T12:35:02ZengNature PortfolioScientific Reports2045-23222024-11-011411910.1038/s41598-024-79654-yPredictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in KoreaHyunji Sang0Jaeyu Park1Soeun Kim2Myeongcheol Lee3Hojae Lee4Sun-Ho Lee5Dong Keon Yon6Sang Youl Rhee7Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of MedicineCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of MedicineCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of MedicineCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of MedicineCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of MedicineGlobal 365MC HospitalCenter for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of MedicineDepartment of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of MedicineAbstract This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuction volumes in patients with obesity. This study used longitudinal cohort data from 2018 to 2023 from five nationwide centers affiliated with 365MC Liposuction Hospital, the largest liposuction hospitals in Korea. Fifteen variables related to patient profiles were integrated and applied to various ML algorithms, including random forest, support vector, XGBoost, decision tree, and AdaBoost regressors. Performance evaluation employed mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) score. Feature importance and RMSE importance analyses were performed to compare the influence of each feature on prediction performance. A total of 9,856 were included in the final analysis. The random forest regressor model best predicted the liposuction volume (MAE, 0.197, RMSE, 0.249, R2, 0.792). Body fat mass and waist circumference were the most important features of the random forest regressor model (feature importance 71.55 and 13.21, RMSE importance 0.201 and 0.221, respectively). Leveraging this model, a web-based application was developed to suggest ideal liposuction volumes. These findings could be used in clinical practice to enhance decision-making and tailor surgical interventions to individual patient needs, thereby improving overall surgical efficacy and patient satisfaction.https://doi.org/10.1038/s41598-024-79654-yObesityLiposuctionMachine learningPredictive value of testsBody fat distributionSurgical procedures |
| spellingShingle | Hyunji Sang Jaeyu Park Soeun Kim Myeongcheol Lee Hojae Lee Sun-Ho Lee Dong Keon Yon Sang Youl Rhee Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea Scientific Reports Obesity Liposuction Machine learning Predictive value of tests Body fat distribution Surgical procedures |
| title | Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea |
| title_full | Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea |
| title_fullStr | Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea |
| title_full_unstemmed | Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea |
| title_short | Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea |
| title_sort | predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi center study in korea |
| topic | Obesity Liposuction Machine learning Predictive value of tests Body fat distribution Surgical procedures |
| url | https://doi.org/10.1038/s41598-024-79654-y |
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