Comparison of the Framingham Risk Score and Deep Neural Network-Based Coronary Heart Disease Risk Prediction
Coronary heart disease (CHD) is one of the top causes of death globally; if suffering from CHD, long time permanent treatments are required. Furthermore, the early detection of CHD is not easy; doctors diagnose it based on many kinds of clinical tests. Therefore, it is effective to reduce the risks of developing CHD by predicting high-risk people who will suffer from CHD. The Framingham Risk Score (FRS) is a gender-specific algorithm used to estimate at 10-years CHD risk of an individual. However, FRS cannot well estimate risk in populations other than the US population. In this study, we have proposed a deep neural network (DNN); this approach has been compared with the FRS and data mining-based CHD risk prediction models in the Korean population. As a result of our experiment, models using data mining have given higher accuracy than FRS-based prediction. Moreover, the proposed DNN has shown the highest accuracy and area under the curve (AUC) score, 82.67%, and 82.64%, respectively.
KeywordsCoronary heart disease Framingham risk score Data mining Deep neural network
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2017R1A2B4010826).
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