Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study.
José Alberto Benítez-AndradesMaría Del Camino Prada-GarcíaRubén García-FernándezMaría Dolores Ballesteros-PomarMaría-Inmaculada González-AlonsoAntonio Serrano-GarcíaPublished in: Digital health (2024)
The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.
Keyphrases
- machine learning
- bariatric surgery
- healthcare
- weight loss
- obese patients
- end stage renal disease
- artificial intelligence
- decision making
- deep learning
- chronic kidney disease
- big data
- newly diagnosed
- ejection fraction
- patients undergoing
- peritoneal dialysis
- prognostic factors
- social media
- risk assessment
- insulin resistance
- current status
- climate change
- health insurance