Realtime Analysis of Sasang Constitution Types from Facial Features Using Computer Vision and Machine Learning
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
Journal of Information and Communication Convergence Engineering
Publication Name
Journal of Information and Communication Convergence Engineering
Volume
22
ISBN/ISSN
2234-8883
Edition
N/A
Issue
3
Pages Count
11
Location
N/A
Publisher
Korea Institute of Information and Communication Engineering
Publisher Url
N/A
Publisher Location
N/A
Publish Date
N/A
Url
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Date
N/A
EISSN
N/A
DOI
10.56977/jicce.2024.22.3.256