Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches.

Abstract

Objectives: To develop and compare machine learning models to classify individuals vulnerable to Hwa-byung (HB) using an existing HB personality scale and to evaluate the efficacy of these models in predicting HB vulnerability.

Methods: We analyzed data from 500 Korean adults (aged 19-44) using HB personality and symptom scales. We used various machine learning techniques, including the random forest classifier (RFC), XGBoost classifier, logistic regression, and their ensemble method (RFC-XGC-LR). The models were developed using recursive feature elimination with cross-validation for feature selection and evaluated using multiple performance metrics, including accuracy, precision, recall, specificity, and area under the receiver operating characteristic curve (AUROC).

Results: The 16 items on the HB personality scale were identified as optimal features to predict high HB symptom scores requiring further clinical evaluation. The ensemble model slightly outperformed the other models, with an accuracy of 0.80 and an AUROC of 0.86, in the test set. Notably, item 16 ("I often feel guilty easily") of the HB personality scale showed the greatest importance in predicting HB vulnerability across all models. Although all models showed consistent performance across training, validation, and test sets, the RFC model exhibited signs of slight overfitting, with a higher AUROC of 0.97 in the training dataset compared to 0.85 in the validation and 0.86 in the test datasets.

Conclusion: Machine learning models, particularly the ensemble method, show capabilities promising for screening individuals with high HB symptom scores based on personality traits, potentially facilitating early referral for clinical evaluation. These models can improve the efficiency and accuracy of the HB risk assessment in clinical settings, potentially aiding early intervention and prevention strategies.