# Model for Predicting the Effect of Sibutramine Therapy in Obesity
## 4 Discussion  
Sibutramine was chosen as a weight loss medicine because our patients did not have significant cardiovascular pathology, and sibutramine has a tablet form of use and an inexpensive price, which is an important advantage over liraglutide. In this study, models were created to predict the effect of sibutramine treatment with a prediction horizon of 3 months and 6 months. During model validation, it was determined that without using complex parameters such as GLP-1, GIP, and others, it is possible to obtain a model of sufficient quality for application in clinical practice. No other study can propose a model that works with a set of parameters and measurements that can be performed in a clinic setting within a maximum of two weeks with a limited budget. This comparative advantage can provide healthcare practitioners with greater confidence in treatment decisions, providing a valuable tool to improve patient care and outcomes. In more detail, the model for 3 months without complex parameters had a classification quality of 71% for the XGBoost model in such a case, while high precision (76%) and recall (75%) metrics were observed for the \"weight loss \(<\) 5%\" class. From this result, it can be concluded that the model performs well in estimating the probability of unsuccessful treatment, highlighting factors that complicate weight loss. Conversely, the lower ability to accurately estimate positive treatment outcome is due to numerous factors, such as sport regime, and adherence to doctor's prescription during obesity treatment, which could not be considered by the ML model. Additionally, the models perform the classification task better than a random classifier, and they adequately utilize the input parameters. This can be demonstrated on the ROC and PR curve plots (Figure 4), and while comparing the performance of our model to the model using only the patient's age and BMI, the quality is 12% higher. For the convenience of medical professionals, the ability to predict further treatment progress based on 3-month weight loss results was added. For this type of model, 80% and 84% results were obtained with a different set of parameters. Using this prediction mode, the specialist will be able to adjust the treatment strategy and thus maintain the weight loss rate. The addition of GLP-1, GIP, ghrelin, miR142, and sST2 parameters improved the quality of the classifiers, achieving 75% for 3 months and 84% for 6 months. Such models will be more useful for the work of a research scientist who seeks to understand the mechanisms of action of a particular drug at the hormonal and molecular genetic level. To make some hypotheses on the cognitive process of the model, we analyzed received explanation plots constructed with the SHAP framework, which uses Shapley values for evaluating the impact on the target result of the model. Results for all four models are shown on Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Accuracy** & **F1** & **Precision** & **Recall** & **ROC AUC** \\ \hline \multirow{2}{*}{3 months} & 71.14\% & 62.31\% & 76.17\% & 75\% & 0.72 \\  & (\(\pm\)2.91\%) & (\(\pm\)3\%) & (\(\pm\)2.43\%) & (\(\pm\)3.19\%) & (\(\pm\)0.03) \\ \hline \multirow{2}{*}{3 months with scientific parameters} & 75.34\% & 69.92\% & 75.44\% & 91.49\% & 0.75 \\  & (\(\pm\)1.84\%) & (\(\pm\)2.39\%) & (\(\pm\)1.45\%) & (\(\pm\)2.24\%) & (\(\pm\)0.02) \\ \hline \multirow{2}{*}{6 months} & 79.55\% & 79.53\% & 78.26\% & 81.82\% & 0.87 \\  & (\(\pm\)2.95\%) & (\(\pm\)3.01\%) & (\(\pm\)3.19\%) & (\(\pm\)5.77\%) & (\(\pm\)0.04) \\ \hline \multirow{2}{*}{6 months with scientific parameters} & 84.09\% & 84.08\% & 85.71\% & 81.82\% & 0.89 \\  & (\(\pm\)3.23\%) & (\(\pm\)3.25\%) & (\(\pm\)3.55\%) & (\(\pm\)5.28\%) & (\(\pm\)0.04) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification metrics. To utilize the trained ML models and interpretation algorithms, we developed a web application using Flask.