An interpretable prediction model for non-invasive identification of the phlegm-dampness constitution based on machine learning.

INTRODUCTION

In traditional Chinese medicine (TCM), constitutional types influence disease susceptibility. Phlegm-Dampness Constitution (PDC), a common subtype, has been linked to elevated risk of metabolic diseases. Therefore, early identification of PDC is essential for implementing early interventions and disease prevention strategies. This study aimed to develop and evaluate interpretable machine learning models for the identification of PDC based on routinely available clinical variables and dietary preferences.

METHODS

We analyzed 139 participants (96 PDC and 43 Balanced Constitution [BC]). Seventeen clinical and biochemical variables were screened using least absolute shrinkage and selection operator regression, and logistic regression, random forest, support vector machine, and Extreme Gradient Boosting classifiers were developed. The dataset was divided into training and test sets, and three-fold cross-validation was applied. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), cut-off value, sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV), and negative predictive value (NPV), while model outputs were interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

The logistic regression model using waist circumference and triglycerides (TG), with age and body mass index (BMI) as covariates, achieved an AUC of 0.983, with a sensitivity of 0.945 and a specificity of 0.970. A second fully non-invasive model using systolic blood pressure, diastolic blood pressure, and waist circumference, with the same covariates, also showed good discrimination and was retained as a candidate model. SHAP analysis provided individual-level explanations of model predictions and identified BMI and TG as key clinical contributors. In addition, we constructed a PDC identification model based on dietary preferences. The logistic regression model showed good discriminative performance in the training data (AUC = 0.891, specificity = 0.964) and the testing data (AUC = 0.838, specificity = 0.894). SHAP indicated that scorching and salty preferences shifted predictions toward PDC, whereas a light preference shifted predictions toward BC.

DISCUSSION: In summary, this study developed interpretable models to improve PDC identification, including a fully non-invasive model, thereby potentially supporting early risk identification and preventive strategies for metabolic diseases in clinical settings.