Application of Machine Learning on Voice Signals to Classify Body Mass Index - Based on Korean Adults in the Korean Medicine Data Center (머신러닝 기반 음성분석을 통한 체질량지수 분류 예측 - 한국 성인을 중심으로)

Junho Kim (김준호); Ki-Hyun Park (박기현); Hoseok Kim (김호석); Siwoo Lee (이시우); Kim Sang-Hyuk (김상혁)

doi:10.7730/JSCM.2021.33.4.1

Application of Machine Learning on Voice Signals to Classify Body Mass Index - Based on Korean Adults in the Korean Medicine Data Center

Journal of Sasang Constitution and Immune Medicine
Abbr : JSCIM
2021, 33(4), pp.1-9
DOI : 10.7730/JSCM.2021.33.4.1
Publisher : The Society of Sasang Constitution and Immune Medicine
Research Area : Medicine and Pharmacy > Korean Medicine
Received : September 8, 2021
Accepted : October 18, 2021
Published : December 31, 2021

Junho Kim ¹, Ki-Hyun Park ¹, Hoseok Kim ¹, Siwoo Lee ¹, Kim Sang-Hyuk ¹

¹한국한의학연구원

Accredited

ABSTRACT

Objectives The purpose of this study was to check whether the classification of the individual’s Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

KEYWORDS

Machine learning, Voice, Body Mass Index, Convolutional neural network

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* References for papers published after 2023 are currently being built.

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Journal of Sasang Constitution and Immune Medicine 2023 KCI Impact Factor : 0.42

Application of Machine Learning on Voice Signals to Classify Body Mass Index - Based on Korean Adults in the Korean Medicine Data Center

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* References for papers published after 2023 are currently being built.

Journal of Sasang Constitution and Immune Medicine 2023 KCI Impact Factor : 0.42

Application of Machine Learning on Voice Signals to Classify Body Mass Index - Based on Korean Adults in the Korean Medicine Data Center

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