Work

Realtime Analysis of Sasang Constitution Types from Facial Features Using Computer Vision and Machine Learning

Year: 2024

Type: article

Abstract: Sasang constitutional medicine (SCM) is one of the best traditional therapeutic approaches used in Korea.SCM prioritizes personalized treatment that considers the unique constitution of an individual and encompasses their physical characteristics, personality traits, and susceptibility to specific diseases.Facial features are essential for diagnosing Sasang constitutional types (SCTs).This study aimed to develop a real-time artificial intelligence-based model for diagnosing SCTs using facial images, building an SCTs prediction model based on a machine learning method.Facial features from all images were extracted to develop this model using feature engineering and machine learning techniques.The fusion of these features was used to train the AI model.We used four machine learning algorithms, namely, random forest (RF), multilayer perceptron (MLP), gradient boosting machine (GBM), and extreme gradient boosting (XGB), to investigate SCTs.The GBM outperformed all the other models.The highest accuracy achieved in the experiment was 81%, indicating the robustness of the proposed model and suitability for real-time applications.

Source: Journal of information and communication convergence engineering

DOI: 10.56977/jicce.2024.22.3.256

Authors: Abdullah, Shah Mahsoom Ali, Hee-Cheol Kim

Institutions: James Cook University, Inje University

Language: English

Topic: Traditional Chinese Medicine Studies

Subfield: Complementary and alternative medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion