A Novel Method in Predicting Hypertension Using Facial Images

Abstract

Hypertension has been a crucial public health challenge among adults. This study aimed to develop a novel method for non-contact prediction of hypertension using facial characteristics such as facial features and facial color. The data of 1099 subjects (376 men and 723 women) analyzed in this study were obtained from the Korean Constitutional Multicenter Study of Korean medicine Data Center (KDC) at the Korea Institute of Oriental Medicine (KIOM). Facial images were collected and facial variables were extracted using image processing techniques. Analysis of covariance (ANCOVA) and Least Absolute Shrinkage and Selection Operator (LASSO) were performed to compare and identify the facial characteristic variables between the hypertension group and normal group. We found that the most distinct facial feature differences between hypertension patients and normal individuals were facial shape and nose shape for men in addition to eye shape and nose shape for women. In terms of facial colors, cheek color in men, as well as forehead and nose color in women, were the most distinct facial colors between the hypertension groups and normal individuals. Looking at the AUC value, the prediction power for women is better than men. In conclusion, we managed to explore and identify the facial characteristics variables related to hypertension. This study may provide new evidence in the validity of predicting hypertension using facial characteristics.