Machine learning model for predicting the cold-heat pattern in Kampo medicine: a multicenter prospective observational study.

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The purpose of this study was to predict the four cold-heat patterns in patients who have the subjective symptoms of the cold-heat pattern described in the International Classification of Diseases Traditional Medicine Conditions - Module 1 by applying a machine learning algorithm. Subjects were first-visit Kampo outpatients at six institutions who agreed to participate in this multicenter prospective observational study. The cold pattern model and the heat pattern model were created separately with 148 symptoms, body mass index, blood pressure (systolic and diastolic), age, and sex. Along with a single cold or heat pattern, the tangled heat/cold pattern is defined as being predicted by both cold and heat patterns, while the moderate (heat/cold) pattern is defined as being predicted by neither the cold pattern nor the heat pattern. We included 622 participants (mean age ±standard deviation, 54.4 ± 16.9; with female 501). The accuracy, macro-recall, precision, and F1-score of a combination of the two prediction models were 96.7%, 93.2%, 85.6%, and 88.5% respectively. The important items were compatible with the definitions of the cold-heat pattern. We developed a prediction model on cold-heat patterns with data from patients whose subjective cold/heat-related symptoms matched the cold-heat pattern diagnosis by the physician.