Why the study?
Deep Neural Networks can be applied to healthcare data to extract hidden information and support clinical decision-making for cardiovascular disease prediction.
Does a Deep Neural Network model using 14 parameters accurately predict cardiovascular disease risk compared to existing methods?
Does a Deep Neural Network model using 14 parameters accurately predict cardiovascular disease risk compared to existing methods?
Deep Neural Networks using 14 clinical parameters can effectively predict cardiovascular disease risk with high accuracy.
DNN-based CVD prediction on 303 volunteers may aid risk stratification; leaves open external validation before clinical use.
In recent years, a huge extent of data that contains hidden information is collected by the health care industries. Deep Neural Networks (DNN) have been employed to obtain appropriate decisions and effective results. The obtained results have been validated using confusion matrix and region of interest. In this work, we have used fourteen parameters for the prediction of cardiovascular disease (CVD) of 303 volunteers. The proposed predictive technique predicts that the chance for prediction of the risk level of cardiovascular disease. In this work, the prediction method using deep neural networks showed the highest accuracy. Our proposed method has outperformed the existing methods and can be combined with multimedia technology.
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Aslam et al. (2022) studied this question.
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