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

Abstract Machine Learning is a powerful tool to discover hidden features in various data driven research fields. Obesity involves extremely complex factors, such as biological, physiological, psychological, and environmental factors. A machine learning framework can provide a successful approach to revealing essential risk factors of the complex obesity phenomenon. Over the last two decades, the obesity population (BMI of above 23) in Korea has been rapidly growing. In this work, we assess obesity prediction by utilizing eight Machine Learning algorithms, and identify risk factors of obesity based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2016-2019. We explore age-specific and gender-specific risk factors of obesity for adults (19-79 years old). Our findings show that the risk factors for obesity are sensitive to age and gender under different Machine Learning algorithms. Both male and female 19-39 age groups show the highest performance of over 70% accuracy and ROC while the 60-79 group shows around 65% accuracy and ROC. Both male and female 40-59 age groups achieved the highest performance of over 70% in ROC, but the female achieved lower 70% in accuracy. Our results highlight that the top four significant features in all age gender groups for prediciting obesity are Triglyceride, ALT(SGPT), Glycated hemoglobin, and urine acid. For the accuracy ratio of the classifiers and age groups, there is no big difference in accuracy when the number of features is more than six, except the accuracy ratio decreased in the female 19-39 age group.