## 1 Introduction  
Obesity is a multifactorial disease that leads to uncontrolled weight gain due to low energy expenditure and high-calorie intake, resulting in abnormal or excessive fat accumulation that can harm health [1, 2]. According to the World Health Organization (WHO), in 2016, 39% of adults aged 18 years and older were overweight, and 13% were obese. These records are only increasing yearly; since 1975, the number of people with obesity has tripled worldwide [3]. The anticipated monetary cost of obesity, which represents 5-14% of health spending from 2020 to 2050, is beyond dispute [4]. According to [PERSON] [5], the direct costs of inactivity and obesity account for about 9.4% of national healthcare expenditures in the United States. In comparison, in the case of Colombia, it is estimated to be USD 2158 million [6]. Additionally, the cost accounts for 1.52% of the expenditure of Peru's gross domestic product, and 1.83% in the case of Mexico [7]. Although these estimates of obesity-related spending may not be entirely correct [8], such high spending underscores the importance of obesity screening. Research in the literature suggests that the risk of obesity depends not only on individual factors, but also on demographic characteristics, community infrastructure, socioeconomic conditions, and environmental and other community-specific factors [9; 10; 11; 12]. In lower socioeconomic status groups in some countries, obesity rates have increased by a factor of three or more due to urbanization, dietary and food supply changes, and a decline in physical activity [13; 14]. Due to the elevated mortality rate from non-communicable diseases such as type 2 diabetes, atherosclerotic cardiovascular diseases, and some types of cancer, obesity can result in a life expectancy reduction of up to 20 years [15; 16; 17; 18]. Considering that obesity is preventable [19] and the significant risk of comorbidities and complications that it brings, early detection of obesity is vital, as it can cause diseases such as cardiovascular problems [20], diabetes [21], and asthma [22]. To this end, it is known that obesity has a multifactorial origin [23] and is influenced by variables such as socioeconomic status [24], occupation [25], and habits such as smoking [26] or the amount of physical activity performed [27]. Physical activity and eating habits are considered two main variables in preventing obesity [28]. The root cause of overweight and obesity is an energy imbalance between calories consumed and expended [29]. A typical technique for losing weight is to create a negative energy balance by reducing energy intake, increasing energy expenditure, or both [30]. People put on unhealthy amounts of weight by consuming foods and beverages that contain more energy than they need for their daily activities. Under these circumstances, the body converts the excess energy into fat for later use, thus reaching a state of obesity. Therefore, obesity depends mainly on a healthy diet and regular physical activity [8]. Early detection can result in rapid obesity prevention, and numerous strategies have been researched and evaluated to identify obesity trends. Current approaches to estimating obesity levels include machine learning (ML)-based classification analysis and risk factor (threshold)-based categorizations based on specific criteria. ML-based feature selection approaches and prediction models can identify links between risk factors for obesity. ML techniques can combine data from large patient groups to provide personalized predictions. For obesity, these techniques can be used to develop risk categories and da-ta-based guidance at the policy level. More specifically, ML can offer a patient-specific treatment method by clarifying the unique characteristics of each patient. In addition, individual evaluation of alternative treatments' efficacy and possible side effects may guide treatment selection and patient follow-up.