Traditional Chinese Medicine Constitution Discrimination Model Based on Metabolomics and Random Forest Decision Tree...

Area of Science:

• Biochemistry
• Genetics
• Traditional Chinese Medicine

Background:

• Constitution describes stable individual characteristics influencing health.
• Traditional Chinese Medicine (TCM) classifies individuals into distinct constitutions.
• Objective methods for TCM constitution identification are lacking.

Purpose of the Study:

• To establish a metabolomics-based approach for identifying unique molecular signatures of TCM constitutions.
• To develop and validate a machine learning model for accurate discrimination of nine TCM constitutions.
• To provide an objective and scientific basis for TCM constitution classification.

Main Methods:

• Development of a specialized metabolomics data processing pipeline.
• Identification of unique mass-to-charge (m/z) values specific to each constitution.
• Construction and validation of a random forest decision tree classification model.

Main Results:

• A novel metabolomics data processing method was established.
• Unique m/z values were identified for each of the nine TCM constitutions.
• The random forest model achieved classification accuracy exceeding 80% for all constitutions.
• Classification accuracy correlated with the distinctness of each constitution.

Conclusions:

• The developed method offers a fast and accurate approach for TCM constitution identification.
• This provides an objective representation for clinical classification and judgment of TCM constitutions.
• The study lays a scientific foundation for the modernization of traditional Chinese medicine.