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Comparison of Machine Learning Models and Framingham Risk Score for the prediction of the presence and severity of Coronary Artery Diseases by using Gensini Score

Yao Wang, Kangjun Zhu, Ya Li, Liding Zhao, Qingbo Lv, Guosheng Fu, Wenbin Zhang
DOI: 10.21203/rs.2.12128/v1

Abstract

Background: The risk prediction model for cardiovascular conditions based on the routine information isn’t established. Machine Learning (ML) models offered opportunities to build a promising and accurate prediction system for the presence and severity of Coronary Artery Diseases (CAD). Methods: In order to compare the validation of ML models to Framingham Risk Score (FRS), a total of 2608 inpatients (1669 men, 939 women; mean age 63.16 ± 10.72 years) at our hospital from January 2015 to July 2017 were extracted from electronic medical system with 29 attributes. Four different ML algorithms (Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN)) were acted to build models, based on eight core risk factors and all factors respectively. The Area Under Curve (AUC) of receiver operating characteristic curve was the significant value to show the prediction power for different models. Results: According to the AUC, all of ML algorithms had a better prediction validation than FRS for the presence of CAD, specifically, FRS<LR<RF<KNN<ANN (FRS variables) and FRS<LR=RF=KNN<ANN (all variables). Additionally, ANN could be the best model to predict the presence of CAD (AUC 0.82, Accuracy 0.74). For the severity, only ANN (AUC 0.70, Accuracy 0.65) in the ML models could have a better prediction than FRS (AUC 0.59, Accuracy 0.62). The other three models didn’t get a better AUC than FRS. Conclusions: Compared to an established FRS prediction algorithm, we found all the ML models had a better prediction validation than FRS for the presence of CAD, moreover, ANN had a better prediction than FRS for the severity of CAD.

Keywords
Machine learning, Framingham Risk Score, Coronary Artery Disease, Gensini Score

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Preprint: Please note that this article has not completed peer review.

Comparison of Machine Learning Models and Framingham Risk Score for the prediction of the presence and severity of Coronary Artery Diseases by using Gensini Score

Yao Wang, Kangjun Zhu, Ya Li, Liding Zhao, Qingbo Lv, Guosheng Fu, Wenbin Zhang

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Abstract

Background: The risk prediction model for cardiovascular conditions based on the routine information isn’t established. Machine Learning (ML) models offered opportunities to build a promising and accurate prediction system for the presence and severity of Coronary Artery Diseases (CAD). Methods: In order to compare the validation of ML models to Framingham Risk Score (FRS), a total of 2608 inpatients (1669 men, 939 women; mean age 63.16 ± 10.72 years) at our hospital from January 2015 to July 2017 were extracted from electronic medical system with 29 attributes. Four different ML algorithms (Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN)) were acted to build models, based on eight core risk factors and all factors respectively. The Area Under Curve (AUC) of receiver operating characteristic curve was the significant value to show the prediction power for different models. Results: According to the AUC, all of ML algorithms had a better prediction validation than FRS for the presence of CAD, specifically, FRS<LR<RF<KNN<ANN (FRS variables) and FRS<LR=RF=KNN<ANN (all variables). Additionally, ANN could be the best model to predict the presence of CAD (AUC 0.82, Accuracy 0.74). For the severity, only ANN (AUC 0.70, Accuracy 0.65) in the ML models could have a better prediction than FRS (AUC 0.59, Accuracy 0.62). The other three models didn’t get a better AUC than FRS. Conclusions: Compared to an established FRS prediction algorithm, we found all the ML models had a better prediction validation than FRS for the presence of CAD, moreover, ANN had a better prediction than FRS for the severity of CAD.

Figures

Background

Methods

Results

Discussions

List of Abbreviations

Declarations

References

Tables

Learn more about our company.