Comparison of the Framingham Risk Score and Deep Neural Network-Based Coronary Heart Disease Risk Prediction. Amarbayasgalan, T., Van Huy, P., & Ryu, K. H. Smart Innovation, Systems and Technologies, 156:273–280, 2020. ISBN: 9789811397134doi abstract bibtex 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.
@article{Pham2020,
title = {Comparison of the {Framingham} {Risk} {Score} and {Deep} {Neural} {Network}-{Based} {Coronary} {Heart} {Disease} {Risk} {Prediction}},
volume = {156},
issn = {21903026},
doi = {10.1007/978-981-13-9714-1_30},
abstract = {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.},
journal = {Smart Innovation, Systems and Technologies},
author = {Amarbayasgalan, Tsatsral and Van Huy, Pham and Ryu, Keun Ho},
year = {2020},
note = {ISBN: 9789811397134},
keywords = {Coronary heart disease, Data mining, Deep neural network, Framingham risk score},
pages = {273--280},
}
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