Objective: To establish a prediction model of qi stagnation referring to two existing models. Design: Prospective observational study. Setting: We recruited patients who visited the Kampo Clinic at Keio University from February 2011 to March 2013. Methods: We constructed a random forest algorithm with 202 items as independent variables to predict qi stagnation patterns using full agreement data of the physicians’ diagnosis and the result of two existing scores as a reference standard. To compare the new model with the two existing models, we calculated the discriminant ratio (prediction accuracy), precision, sensitivity (recall), specificity, and F-measure of these models. Results: The number of eligible participants was 1,194, and 29.1% of them were diagnosed with qi stagnation by Kampo physicians. The discriminant ratio, precision, sensitivity, specificity, and F-measure in our new model were 0.960, 0.672, 0.911, 0.964, and 0.774, respectively. Our new model had a significantly higher discriminant ratio than the two existing models. Conclusions: We constructed a better qi stagnation prediction model than the previously established ones. Our results can be utilized to reach an international agreement on qi stagnation pattern diagnosis in traditional East Asian medicine.
ASJC Scopus subject areas
- コンピュータ サイエンスの応用