Key result
Machine learning successfully predicts long-term weight loss outcomes after bariatric surgery.
Why the study?
The study aimed to develop practical post-surgical guidelines from clinical data to identify potential predictors of surgical success and minimize long-term weight regain following surgery.
Can machine learning algorithms identify predictors of long-term weight regain following bariatric surgery?
Observational
Can machine learning algorithms identify predictors of long-term weight regain following bariatric surgery?
Machine learning models can accurately predict long-term weight regain post-bariatric surgery, potentially aiding in the development of targeted post-surgical guidelines.
ML models may aid post-bariatric risk stratification; leaves open prospective validation before clinical use.
Obesity is defined as a body mass index (BMI) of 30 kg/m 2 or greater. Obesity is a complex medical condition characterized by excess body fat that increases the risk of developing co-morbidities like cardiovascular disease (CVD) and diabetes. Excess weight is a global concern with an ever-growing prevalence. This research study aims to develop effective and practical post-surgical guidelines based on clinical data to determine potential predictors of surgical success. With this information, clinicians and patients can work together to minimize long-term weight regain post-surgery. To fulfill this goal, we applied various machine learning (ML) algorithms to model our dataset and provide a list of critical features to target to have more successful long-term weight-loss outcomes. Fortunately, our ML models achieved outstanding Mean Absolute Errors (0.10 - 0.22).
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ZareMehrjardi et al. (2022) conducted an observational in Obesity following bariatric surgery. Machine learning algorithms was evaluated on Mean Absolute Error of machine learning models predicting long-term weight-loss outcomes. Machine learning algorithms successfully modeled clinical data to predict long-term weight-loss outcomes following bariatric surgery, achieving Mean Absolute Errors of 0.10 to 0.22.
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