  Objectives: Metabolic Bariatric Surgery (MBS) is a critical intervention for
patients living with obesity and related health issues. Accurate classification
and prediction of patient outcomes are vital for optimizing treatment
strategies. This study presents a novel machine learning approach to classify
patients in the context of metabolic bariatric surgery, providing insights into
the efficacy of different models and variable types. Methods: Various machine
learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN
with RandomOverSampler, and KNN with SMOTE, were applied to a dataset of 73
patients. The dataset, comprising psychometric, socioeconomic, and analytical
variables, was analyzed to determine the most efficient predictive model. The
study also explored the impact of different variable groupings and oversampling
techniques. Results: Experimental results indicate average accuracy values as
high as 66.7% for the best model. Enhanced versions of KNN and Decision Tree,
along with variations of KNN such as RandomOverSampler and SMOTE, yielded the
best results. Conclusions: 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.
