AUTHOR=Jeon Junhwi , Lee Sunmi , Oh Chunyoung 

TITLE=Age-specific risk factors for the prediction of obesity using a machine learning approach

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.998782

DOI=10.3389/fpubh.2022.998782

ISSN=2296-2565

ABSTRACT=Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this paper is to identify risk factors which predict obesity using machine learning  classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on KNHANES, which used data constructed by the annual survey. Our data includes the total number of 21,100 participants (male 10,000 and female 11,100). We assess obesity prediction by utilizing six machine learning algorithms.  We explore age-specific and gender-specific risk factors of obesity for adults (19-79 years old). Our results highlight that the four most significant features in all age-gender groups for predicting obesity are Triglycerides, ALT (SGPT), Glycated hemoglobin, and Urine acid. Our findings show that the risk factors for obesity are sensitive to age and gender under different machine learning algorithms. Performance is highest for the 19-39 age group of both genders, with over 70\% accuracy and AUC while the 60-79 group shows around 65\% accuracy and AUC. For the 40-59 age groups, the proposed algorithm achieved over 70\% in AUC, but for the females it achieved lower than 70\% accuracy. For all classifiers and age groups, there is no big difference in accuracy ratio when the number of features is more than six, however the accuracy ratio decreased in the female 19-39 age group.