Background: A predictive model of facial feature data was established by machine learning to screen the objective parameters of risk factors of facial morphological features of type 2 diabetes mellitus (T2DM) following the theory of traditional Chinese medicine (TCM). In TCM, a facial inspection is an important way to diagnose patients. Doctors can judge the health status of their patients by observing their facial features. However, the lack of description of the objective parameters and quantitative indicators hinders the development of TCM testing research.
Methods: In this study, the following diagnostic criteria for diabetes developed by the World Health Organization (WHO) in 1999 were used to determine the inclusion and exclusion criteria for T2DM and non-T2DM. T2DM patients and control participants were enrolled in the study, and their facial images were collected. In this study, two facial inspection risk-factor models were constructed, including the "lambda.min" and "lambda.1se" model.
Results: A total of 81 key points in the facial images were screened, and 18 facial morphological parameters were measured. The least absolute shrinkage and selection operator (LASSO) regression model was used to construct T2DM facial inspection risk-factor models. The area under the curves (AUCs) of the "lambda.min" model and the "lambda.1se" model were 0.799 and 0.776, respectively. The predictive efficiency of the two T2DM risk models selected by the LASSO regression model was relatively high. Among the eight parameters, the width of the jaw was the most important of the defined facial features. According to the receiver operating characteristic (ROC) curve analysis of the two prediction models constructed, the two models had good predictive efficiency for T2DM. The AUCs of the two models were 0.695 and 0.682, respectively. And the reproducibility is good. The prediction model was available, which showed that the objective parameters of the facial features recognized by machine learning have a certain value in the automatic prediction of T2DM.
Conclusions: The influence of facial features is physical factor. Thus, the objective parameters of facial features should be specific to differential diagnosis of T2DM.
Keywords: Type 2 diabetes mellitus (T2DM); facial parameters of traditional Chinese medicine (facial parameters of TCM); machine learning.
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