Estimation of obesity levels based on computational intelligence

Abstract

It's commendable that the study included participants from multiple countries providing a more diverse dataset. However, it would be interesting to explore how cultural and regional differences may influence obesity rates and causes. Focusing on students aged 18 to 25 is strategic, as this age range often represents a critical period where lifestyle habits are formed. Understanding factors leading to obesity in this demographic can contribute significantly to preventive strategies. The inclusion of factors such as caloric intake, physical activity, genetics, socioeconomic status, and mental health is comprehensive. Analyzing the interplay between these factors could yield valuable insights into the complex nature of obesity. The use of Decision Trees, Support Vector Machines, and Simple K-Means for analysis is robust. It would be interesting to delve deeper into how each algorithm performs concerning sensitivity, specificity, and predictive accuracy in identifying obesity levels. The mention of a comparative analysis is intriguing. Highlighting the strengths and weaknesses of each algorithm in the context of obesity detection could provide valuable guidance for future researchor practical applications. While 178 participants is a reasonable sample size, it's worth considering the representativeness of this sample concerning the broader population. Additionally, assessing whether the gender distribution in the study reflects regional demographics could be important. Discussing the practical implications of the findings could be beneficial. For instance, how can the results inform interventions or policies to address obesity in young adults in these regions? Given the sensitivity of health-related data, it's essential to address ethical considerations, such as participant consent, data privacy, and the potential impact of the study on participants.