Journal of Sasang Constitutional Medicine (사상체질의학회지)

The Society Of Sasang Constitutional Medicine (사상체질의학회)
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Sasang Constitution Detection Based on Facial Feature Analysis Using Explainable Artificial Intelligence

설명가능한 인공지능을 활용한 안면 특징 분석 기반 사상체질 검출

  • Jeongkyun Kim (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Ilkoo Ahn (KM Data Division, Korea Institute of Oriental Medicine) ;
  • Siwoo Lee (KM Data Division, Korea Institute of Oriental Medicine)
  • 김정균 (한국한의학연구원 한의약데이터부) ;
  • 안일구 (한국한의학연구원 한의약데이터부) ;
  • 이시우 (한국한의학연구원 한의약데이터부)
  • Received : 2024.06.03
  • Accepted : 2024.07.13
  • Published : 2024.07.31
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Abstract

Objectives The aim was to develop a method for detecting Sasang constitution based on the ratio of facial landmarks and provide an objective and reliable tool for Sasang constitution classification. Methods Facial images, KS-15 scores, and certainty scores were collected from subjects identified by Korean Medicine Data Center. Facial ratio landmarks were detected, yielding 2279 facial ratio features. Tree-based models were trained to classify Sasang constitution, and Shapley Additive Explanations (SHAP) analysis was employed to identify important facial features. Additionally, Body Mass Index (BMI) and personality questionnaire were incorporated as supplementary information to enhance model performance. Results Using the Tree-based models, the accuracy for classifying Taeeum, Soeum, and Soyang constitutions was 81.90%, 90.49%, and 81.90% respectively. SHAP analysis revealed important facial features, while the inclusion of BMI and personality questionnaire improved model performance. This demonstrates that facial ratio-based Sasang constitution analysis yields effective and accurate classification results. Conclusions Facial ratio-based Sasang constitution analysis provides rapid and objective results compared to traditional methods. This approach holds promise for enhancing personalized medicine in Korean traditional medicine.

Keywords

Acknowledgement

본 연구는 2024년도 한국한의학연구원 기관주요사업인 "빅데이터 기반 한의 예방치료 원천기술 개발"(Grant No. KSN1732121)의 지원을 받아 수행된 연구이다.

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