Selection of Machine Learning Models for Prescription Decision-Making Based on Text Mining - Focusing on Case Studies of Single Prescriptions in Sasang Constitutional Medicine

Objectives : We analyzed Sasang constitution case reports using text mining and designed a classification algorithm using machine learning to select a model suitable for determining Sasang constitution prescriptions based on text data.Methods: Case reports on Sasang constitution published from January 1, 2000, to December 31, 2023, were collected. A total of 360 papers and 483 cases were identified, from which text was extracted for 253 cases. The extracted texts were preprocessed and tokenized using the Python-based KoNLPy package, and each morpheme was vectorized using TF-IDF values. To select the most suitable classification model for diagnosing Sasang constitution, the performance of five models—Random Forest Classifier, XGBoost, LightGBM, SVM, and Logistic Regression—was evaluated based on accuracy and F1-Score.Results: The highest accuracy was achieved by Random Forest Classifier (0.57037), followed by SVM (0.544444), Logistic Regression (0.518519), LightGBM (0.481481), and XGBoost (0.474074). The F1 score was highest for Random Forest Classifier (0.528), followed by SVM (0.52039), Logistic Regression (0.500861), XGBoost (0.45866), and LightGBM (0.446349).Conclusions: This study is the first to analyze Sasang constitution prescription decisions by applying text mining and machine learning to case reports, providing a concrete research model for follow-up studies. Based on case reports and text data, the most suitable machine learning model for determining Sasang constitution prescriptions is Random Forest Classifier.