Key result
Machine-learning model using lifestyle variables classifies weight levels with 75% accuracy.
Why the study?
Early prediction of obesity risk could help reduce its increasing prevalence, but existing classification often relies on BMI rather than lifestyle factors.
A machine learning model using lifestyle variables without relying on BMI achieved 75% accuracy in classifying obesity levels.
May support BMI-free obesity screening; hypothesis-generating and requires prospective validation before clinical use.
In recent years, the prevalence of obesity and its related co-morbidities have been increasing significantly. Therefore, it is an important challenge to pursue an early prediction of obesity risk that could help in reducing the pace of obesity rise when appropriate interventions are placed, accordingly. The prediction and classification of obesity depend on different factors such as body mass index (BMI) and lifestyle aspects, including eating habits. By focusing on these lifestyles and eating habit factors, we can develop a more holistic approach to weight management and prevention of obesity. The aim of this paper is to propose a machine-learning model that can classify weight levels using lifestyle variables without relying on BMI which enables us to investigate how lifestyle factors affect different levels of weight categorization. Although BMI is the most widely used estimation of obesity, there are other factors that can contribute to gaining weight such as lifestyle factors. The accuracy of our lifestyle-based model reached 75% excluding weight, height, and family history. Our model could serve as a starting point for using an interpretable machine learning model to better understand the effect of lifestyle factors on obesity levels.
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Khater et al. (2023) studied Obesity. Machine-learning model using lifestyle variables was evaluated on Accuracy of weight level classification. A machine-learning model classifying weight levels using lifestyle variables, excluding weight, height, and family history, achieved an accuracy of 75%.
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