Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology

Changgyun Kim and Sekyoung Youm
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Changgyun Kim: Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea
Sekyoung Youm: Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea

Sustainability, 2020, vol. 12, issue 9, 1-17

Abstract: Measuring exact obesity rates is challenging because the existing measures, such as body mass index (BMI) and waist-to-height ratio (WHtR), do not account for various body metrics and types. Therefore, these measures are insufficient for use as health indices. This study presents a model that accurately classifies abdominal obesity, or muscular obesity, which cannot be diagnosed with BMI. Using the model, a web-based calculator was created, which provides information on obesity by predicting healthy ranges, and obesity, underweight, and overweight values. For this study, musculoskeletal mass and body composition mass data were obtained from Size Korea. The groups were divided into four groups, and six body circumference values were used to classify the obesity levels. Of the four learning models, the random forest model was used and had the highest accuracy (99%). This enabled us to build a web-based tool that can be accessed from anywhere and can measure obesity information in real-time. Therefore, users can quickly receive and update their own obesity information without using existing high-cost equipment (e.g., an Inbody machine or a body-composition analyzer), thereby making self-diagnosis convenient. With this model, it was easy to recognize and manage health conditions by quickly receiving and updating information on obesity without using traditional, expensive equipment, and by providing accurate information on obesity, according to body types, rather than information such as BMI, which are identified based on specific body characteristics.

Keywords: deep learning; data mining; analysis; body index; healthcare; big data; body mass index; deep neural network; classification; variable selection; regression; self-obesity diagnosis; web service; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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