Prediction Model for Hypertriglyceridemia Based on Naive Bayes Using Facial Characteristics


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 11, pp. 433-440, Nov. 2019
https://doi.org/10.3745/KTSDE.2019.8.11.433, Full Text:
Keywords: Machine Learning, Facial Characteristics, Hypertriglyceridemia, Predictive model
Abstract

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.


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Cite this article
[IEEE Style]
L. Juwon and L. B. Ju, "Prediction Model for Hypertriglyceridemia Based on Naive Bayes Using Facial Characteristics," KIPS Transactions on Software and Data Engineering, vol. 8, no. 11, pp. 433-440, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.433.

[ACM Style]
Lee Juwon and Lee Bum Ju. 2019. Prediction Model for Hypertriglyceridemia Based on Naive Bayes Using Facial Characteristics. KIPS Transactions on Software and Data Engineering, 8, 11, (2019), 433-440. DOI: https://doi.org/10.3745/KTSDE.2019.8.11.433.