Development of an interpretable machine learning model associated with genetic indicators to identify Yin-deficiency constitution.
Chin Med
; 19(1): 71, 2024 May 15.
Article
em En
| MEDLINE
| ID: mdl-38750482
ABSTRACT
BACKGROUND:
Traditional Chinese Medicine (TCM) defines constitutions which are relevant to corresponding diseases among people. As one of the common constitutions, Yin-deficiency constitution influences a number of Chinese population in the disease onset. Therefore, accurate Yin-deficiency constitution identification is significant for disease prevention and treatment.METHODS:
In this study, we collected participants with Yin-deficiency constitution and balanced constitution, separately. The least absolute shrinkage and selection operator (LASSO) and logistic regression were used to analyze genetic predictors. Four machine learning models for Yin-deficiency constitution classification with multiple combined genetic indicators were integrated to analyze and identify the optimal model and features. The Shapley Additive exPlanations (SHAP) interpretation was developed for model explanation.RESULTS:
The results showed that, NFKBIA, BCL2A1 and CCL4 were the most associated genetic indicators with Yin-deficiency constitution. Random forest with three genetic predictors including NFKBIA, BCL2A1 and CCL4 was the optimal model, area under curve (AUC) 0.937 (95% CI 0.844-1.000), sensitivity 0.870, specificity 0.900. The SHAP method provided an intuitive explanation of risk leading to individual predictions.CONCLUSION:
We constructed a Yin-deficiency constitution classification model based on machine learning and explained it with the SHAP method, providing an objective Yin-deficiency constitution identification system in TCM and the guidance for clinicians.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Chin Med
Ano de publicação:
2024
Tipo de documento:
Article