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Published on: February 7, 2014
Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and
Alexandre Vallée1,2,3, Alexandre Cinaud1,2,3, Vincent Blachier1,2,3
1Paris-Descartes University.
Insights
Artificial neural networks (ANNs) show promise for predicting coronary heart disease (CHD) risk. Models incorporating the pulse wave velocity (PWV) index achieved high accuracy, offering a non-invasive diagnostic approach.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Cardiovascular diseases, including coronary heart disease (CHD), are leading global causes of mortality.
- Traditional risk factors do not fully predict CHD, highlighting the need for novel prediction methods.
- Developing advanced diagnostic tools for CHD risk assessment is crucial for public health.
Purpose of the Study:
- To develop an artificial neural network (ANN)-based diagnostic model for predicting CHD risk.
- To integrate clinical, hemodynamic factors, and the aortic pulse wave velocity (PWV) index into the predictive model.
- To explore the efficacy of ANNs in enhancing CHD risk stratification.
Main Methods:
- A cohort of 437 patients (99 CHD, 338 non-CHD) was analyzed between 2012 and 2017.
- Theoretical PWV was calculated based on patient demographics and physiological data.
- Multilayered perceptron ANNs were employed, with model performance evaluated by accuracy metrics.
Main Results:
- ANN models demonstrated predictive accuracy ranging from 0.63 to 0.93.
- The highest accuracy was achieved using a multilayer perceptron with three hidden layers.
- Optimal models incorporated biological factors, carotid plaque, and the PWV index.
Conclusions:
- ANN models integrating the PWV index offer a promising, non-invasive method for CHD risk prediction.
- These models can aid clinicians in making informed decisions regarding patient management.
- The study supports the use of AI-driven approaches for cardiovascular risk assessment.
Background:
Cardiovascular disease, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. CHD is not entirely predicted by classic risk factors; however, they are preventable. Facing this major problem, the development of novel methods for CHD risk prediction is of practical interest. The purpose of our study was to construct an artificial neural networks (ANNs)-based diagnostic model for CHD risk using a complex of clinical and haemodynamics factors of this disease and aortic pulse wave velocity (PWV) index.
Methods:
A total of 437 patients were included from 2012 to 2017: 99 CHD and 338 non-CHD patients. Theoretical PWV was calculated, on 93 patients free of hypertension, diabetes and CHD, according to age, blood pressure, sex and heart rate. The results were expressed as an index [(measured PWV - theoretical PWV)/theoretical PWV] for each patient. The original database for ANNs included clinical, haemodynamic and laboratory characteristics. Multilayered perceptron ANNs architecture were applied. The performance of prediction was evaluated by accuracy values based on standard definitions.
Results:
By changing the types of ANNs and the number of input factors applied, we created models that demonstrated 0.63-0.93 accuracy. The best accuracy was obtained with ANNs topology of multilayer perceptron with three hidden layers for models, parameters included by both biological factors, carotid plaque and PWV index.
Conclusion:
ANNs models including a PWV index could be used as promising approaches for predicting CHD risk without the need for invasive diagnostic methods and may help in the clinical decision.
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