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Improvement in the Prediction of Coronary Heart Disease Risk by Using Artificial Neural Networks
Orit Goldman1, Orit Raphaeli, Eran Goldman
1Ono Academic College, Kiryat Ono, Israel (Dr O. Goldman); Ariel University, Kiryat Hamada Ariel, Israel (Dr Raphaeli); Bar Ilan University, Ramat Gan, Israel (Mr E. Goldman); and Tel Aviv University, Ramat Aviv, Israel (Dr Leshno).
Insights
An artificial neural network (ANN) model shows promise for predicting coronary heart disease (CHD) risk, outperforming the traditional Framingham risk score (FRS). This advanced analytical approach offers a potentially better screening tool for identifying individuals at high risk of CHD.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Analytics
Background:
- Coronary heart disease (CHD) is a leading cause of global mortality and morbidity.
- While preventable, CHD is not fully predictable by traditional risk factors.
- Accurate CHD risk prediction is vital for clinical cardiology and public health.
Purpose of the Study:
- To develop and evaluate an artificial neural network (ANN) model for predicting CHD risk.
- To compare the predictive performance of the ANN model against the established Framingham risk score (FRS).
- To explore advanced analytical methods for enhancing CHD risk assessment.
Main Methods:
- Utilized a multilayer perceptron ANN architecture on the Framingham Heart Study (FHS) offspring cohort (n=3066).
- Compared ANN model performance against the FRS using metrics including lift, gains, ROC, and precision-recall curves.
- Analyzed performance across different risk percentiles and diagnostic thresholds.
Main Results:
- The ANN model demonstrated superior performance over the FRS in lift and gain curves for top risk percentiles.
- For higher risk scores, the ANN exhibited improved sensitivity and specificity compared to the FRS on ROC analysis, despite a lower AUC.
- The ANN achieved significantly better precision-recall results, indicated by a higher AUC.
Conclusions:
- The ANN model presents a promising advancement for predicting CHD risk.
- The ANN serves as an effective screening procedure for identifying high-risk individuals.
- This study highlights the potential of advanced analytics in cardiovascular risk stratification.
Background And Objectives:
Cardiovascular diseases, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. Although CHD cannot be entirely predicted by classic risk factors, it is preventable. Therefore, predicting CHD risk is crucial to clinical cardiology research, and the development of innovative methods for predicting CHD risk is of great practical interest. The Framingham risk score (FRS) is one of the most frequently implemented risk models. However, recent advances in the field of analytics may enhance the prediction of CHD risk beyond the FRS. Here, we propose a model based on an artificial neural network (ANN) for predicting CHD risk with respect to the Framingham Heart Study (FHS) dataset. The performance of this model was compared to that of the FRS.
Methods:
A sample of 3066 subjects from the FHS offspring cohort was subjected to an ANN. A multilayer perceptron ANN architecture was used and the lift, gains, receiver operating characteristic (ROC), and precision-recall predicted by the ANN were compared with those of the FRS.
Results:
The lift and gain curves of the ANN model outperformed those of the FRS model in terms of top percentiles. The ROC curve showed that, for higher risk scores, the ANN model had higher sensitivity and higher specificity than those of the FRS model, although its area under the curve (AUC) was lower. For the precision-recall measures, the ANN generated significantly better results than the FRS with a higher AUC.
Conclusions:
The findings suggest that the ANN model is a promising approach for predicting CHD risk and a good screening procedure to identify high-risk subjects.
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