본문 바로가기
  • Home

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 Constitutional Medicine
  • Abbr : J Sasang Constitut Med
  • 2021, 33(4), pp.1-9
  • DOI : 10.7730/JSCM.2021.33.4.1
  • Publisher : The Society Of Sasang Constitutional 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.

Citation status

* References for papers published after 2022 are currently being built.