Discrimination of prediction models between cold-heat and deficiency-excess patterns

Abstract

Objective: The purpose of this study was to extract important patient questionnaire items by creating random forest models for predicting pattern diagnosis considering an interaction between deficiency-excess and cold-heat patterns.

Design: A multi-centre prospective observational study.

Setting: Participants visiting six Kampo speciality clinics in Japan from 2012 to 2015.

Main outcome measure: Deficiency-excess pattern diagnosis made by board-certified Kampo experts.

Methods: We used 153 items as independent variables including, age, sex, body mass index, systolic and diastolic blood pressures, and 148 subjective symptoms recorded through a questionnaire. We sampled training data with an equal number of the different patterns from a 2 × 2 factorial combination of deficiency-excess and cold-heat patterns. We constructed the prediction models of deficiency-excess and cold-heat patterns using the random forest algorithm, extracted the top 10 essential items, and calculated the discriminant ratio using this prediction model.

Results: BMI and blood pressure, and subjective symptoms of cold or heat sensations were the most important items in the prediction models of deficiency-excess pattern and of cold-heat patterns, respectively. The discriminant ratio was not inferior compared with the result ignoring the interaction between the diagnoses.

Conclusions: We revised deficiency-excess and cold-heat pattern prediction models, based on balanced training sample data obtained from six Kampo speciality clinics in Japan. The revised important items for diagnosing a deficiency-excess pattern and cold-heat pattern were compatible with the definition in the 11^th version of international classification of diseases.

Keywords: Decision support system; International Classification of Diseases; Machine learning; Traditional medicine pattern.