# Model for Predicting the Effect of Sibutramine Therapy in Obesity
## 1 Introduction  
Obesity is one of the most common non-communicable pandemics and, with its visceral nature, it is naturally accompanied by insulin resistance and, subsequently, the development of diabetes mellitus (DM), arterial hypertension (AH), and dyslipidemia (DLP) (hypertriglyceridemia and a decrease in high-density lipoprotein (HDL) concentration). Since obesity is the cornerstone of this cluster of metabolic pathologies, weight loss is a key task in their correction. Thus, a 5% weight loss can make a huge contribution to improving metabolic health and become an impetus for further active weight loss [1]. This parameter (5% weight loss) is included in the obesity treatment algorithms as the minimum value indicating the effectiveness of treatment [2]. Unfortunately, none of the existing methods provide effective weight loss in 100% of patients, which requires a personalized predictor-based approach to choose the most effective treatment method for each patient. To solve this problem, artificial intelligence (AI) with machine learning models (MLMs) has been actively studied and applied in recent years. MLMs make it possible to predict the response to therapy, and their implementation in healthcare can significantly increase its effectiveness [3, 4]. Based on the data from literature reviews, it was shown that the majority of studies (in 18 of 22 studies) using AI-based models showed higher prediction accuracy compared to traditional statistical approaches [5]. Another important task that MLMs can solve is minimizing the time constraints related to a therapy choice. The time for making a decision is limited during an outpatient visit to a doctor. For the optimal choice of individualized therapy for each patient, it is necessary to consider a multitude offactors. During the outpatient appointment, the specialist should have time to interview the patient, enter the patient's information into the medical information system (MIS), schedule the patient with other specialists if necessary, and write referrals for examinations and prescriptions for medications, having previously assessed the presence or absence of contraindications to a particular class of drugs and weighing the chances of successful treatment. In this situation, computer-based decision support systems (DSSs) based on AI and MLMs can significantly reduce time costs and optimize choices. At the moment, MLMs are used in various fields of medicine. In radiology and oncology, artificial intelligence can help characterize the findings on images, automatically identify and classify them as benign or malignant, and calculate and plan radiation therapy [6]. In cardiology, machine learning significantly increases the accuracy of predicting cardiovascular risk, increasing the number of identified patients who may benefit from preventive treatment [7]. In endocrinology, DSSs are used to assess the risk of developing diseases, particularly diabetes, in individuals to ensure timely and optimal preventive measures [8]. DSSs are also used to evaluate the achievement of target values for primary metabolic parameters (they can be intended for both the doctor and the patient), for which data from activity tracking bracelets, electronic nutrition diaries, and devices monitoring glycemia are used. For example, the DiaCompanion application developed by our colleagues for patients with gestational diabetes [9] predicts changes in blood glucose concentration after taking a particular meal, allowing patients to adjust their food plans and prevent hyperglycemia. DSSs can also help in selecting the optimal type and volume of therapy during outpatient visits [10]. Finally, MLMs and DSSs based on them can be useful in predicting the risk of complications, death, and choosing the optimal volume and time of correction in urgent situations for both in patients with diabetes and in the general population. As an example, [PERSON] et al. 's research can be indicated, as well as that of [PERSON] et al. [11; 12].