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.
- Decision support system
- International classification of diseases
- Machine learning
- Traditional medicine pattern