# Model for Predicting the Effect of Sibutramine Therapy in Obesity
## 1 Introduction
There is evidence showing the effectiveness of using artificial intelligence in decision support for various dietary interventions in obesity management [13; 14; 15; 16]. The use of machine learning techniques to study the interrelationships between psychological components (anxiety, depression) and changes in BMI was evaluated, and a high degree of predictive precision of the result was shown in one previous study [17]. There is evidence that in-depth phenotyping of obesity can potentially determine therapeutic planning for various subtypes of obesity [18]. However, the use of AI to predict the response to drug therapy for weight loss is just beginning and the data in the literature are limited in scope and not presented for all drugs. In particular, the development of such models using sibutramine is described in only two studies [19; 20]. Previously, [PERSON] and a team of medical experts from Almazov National Medical Research Center proposed a system which allows medical specialists to calculate the future effects of the weight loss drug sibutramine [19]. The authors implemented a binary classification model with a successful prediction accuracy of about 86%. On the other hand, the author of the second model uses hard-to-access and expensive analyses such as GLP-1, ghrelin, and others. This makes prediction on the first visit very difficult, which does not suit our goal of making weight loss forecasts based on a limited number of parameters. Continuing this research, the utilization of regression modeling was suggested to predict specific outcomes in a subsequent study [20]. Furthermore, the methodology was extended to several medications: sibutramine, metformin, and liraglutide. However, the set of patient parameters used in the models does not also meet the target requirements for the research. In the case of a shortened, minimally necessary, and feasible set of parameters for collection, no additional methodologies are provided to achieve sufficient quality. No research on performance decline is provided for a situation where we use a smaller subset of parameters when a full set of parameters is not available. Consequently, as a main aim in developing the methodology, we can declare the ability of a model to provide good accuracy with small, noisy, and limited-in-predictor-variables data. For tasks involving small datasets, complex models may not always be the best choice due to the risk of overfitting. In such scenarios, tree-based models, such as decision trees,random forests, and gradient boosting machines, have consistently demonstrated superior performance. These models excel at capturing complex relationships within the data while remaining interpretable and robust. Despite the growing popularity of deep learning methods, tree-based models continue to outperform them on typical tabular data [21], especially when datasets are small. Moving from model architecture improvement, we can investigate solutions related to the valuation of training data and filtering. This includes the identification of label errors, and filtration of noisy, harmful, or uninformative data with a negative impact on model quality. The Data Shapley method [22] sufficiently improved the Alzheimer's disease factors prediction on MRI scans according to the recent research [23]. This method is commonly used for filtering wrongly labeled data, noisy samples, or mistakes. The applicability of this approach has been proven on the dataset with 756 images, and applicability for small data, as in this project, has not been previously researched, although it may seem that in small data no samples can be removed. Therefore, we can experiment with the methodology and integrate it into our task to analyze the ability to improve the accuracy of weight loss prognosis for patients. There are some improvements of this method [23; 24], but according to guidelines and experiments from the study, the classical Data Shapley method perfectly fits the data valuation and filtration tasks, especially when researchers work with small datasets.