Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis.

ABSTRACT:

Background

Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features.

Methods

Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis.

Results

The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general i

SUBMITTER: Park SY 

PROVIDER: S-EPMC7903349 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature