Abstract
Background Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.
Highlights
Based on the innate inheritance of the human body and the influence of acquired factors, Body Constitution (BC) is comprehensively expressed through various aspects such as psychological state, viscera function, metabolic function, and human morphology
Nine Body constitution (BC) types are combined into combinations containing three different BC types, resulting in a total of 84 C39 BC combinations. e feature selection algorithm is used to screen out the top k, questions with the highest importance score in each BC combination. ese questions are combined with options and BC labels in the original dataset to obtain a dataset for training the body constitution identification model (BCIM). ere are nine constitution labels in the dataset, including Balanced Constitution, Qi-deficient Constitution, Yang-deficient Constitution, Yin-deficient Constitution, Phlegm-dampness Constitution, Damp-heat Constitution, Stagnant Blood Constitution, Stagnant Qi Constitution, and Inherited Special Constitution
To visually compare the performance of the classifier, we take the highest accuracy of the 5-fold as the accuracy of the model and extract the evaluation metrics data of this point. e performance is shown in Figure 3. e values obtained by linear discriminate analysis (LDA), artificial neural network (ANN), and support vector machine (SVM) models are all above 90%. is shows that identifying BC by feature selection and the classifier is feasible and effective
Summary
Based on the innate inheritance of the human body and the influence of acquired factors, Body Constitution (BC) is comprehensively expressed through various aspects such as psychological state, viscera function, metabolic function, and human morphology. Body constitution (BC) is the abstract concept indicating the state of a person’s health in Traditional Chinese Medicine (TCM). Is paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. E experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It improves the accuracy of recognizing BC. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient’s BC types
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