Predicting Obesity Levels Through Dietary Patterns: A Machine Learning Approach for Health Analytics and Intervention
Keywords:
Public health, Obesity, Eating habits, Feature selection, Predictive modelling, Machine learning.Abstract
Obesity is a widespread worldwide health problem, caused by a variety of variables such as genetics, environment, and lifestyle. An important factor that contributes to obesity is eating habits, so it is essential to comprehend the connection between dietary choices and degrees of obesity. This study investigates the utilization of machine learning (ML) methods to forecast obesity levels by analyzing eating patterns. Here, we are considering a comprehensive dataset that includes a wide range of demographic information, food patterns, and obesity levels of individuals. Multiple machine learning techniques, such as Decision Trees, Support Vector Machines, Random Forests, and Neural Networks, are used to create prediction models. Feature selection approaches are utilized to determine the most impactful dietary aspects that contribute to obesity. The proposed methodology evaluates the model's performance by utilizing metrics like as accuracy, precision, recall, and F1-score. Moreover, machine learning models exhibit encouraging prediction abilities, with specific algorithms surpassing others in terms of accuracy and dependability. In addition, the analysis of feature importance identifies particular food groups and consumption patterns that are highly linked to obesity, offering valuable information for focused interventions and individualized dietary advice. This study makes a valuable contribution to the expanding area of predictive healthcare analytics by providing a data-driven method to tackle difficulties connected to obesity. The results have ramifications for public health policy, nutrition education programs, and personalized healthcare initiatives, with the goal of reducing the obesity pandemic and encouraging better lifestyles.
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