Key result
Novel Predator Crow Optimization deep neural network outperforms existing models with ~97% cardiovascular prediction accuracy.
Why the study?
Cardiovascular diseases are among the leading causes of death worldwide, and the Internet of Things provides an opportunity to enable healthcare systems to identify these conditions.
A novel PCO-DNN framework demonstrates high accuracy (96.67%) in predicting cardiovascular diseases, outperforming existing classification models.
Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors.
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Alqurashi et al. (2023) studied Cardiovascular diseases. Predator Crow Optimization and Deep Neural Network (PCO-DNN) framework vs. Existing classification models was evaluated on Prediction and classification accuracy of cardiovascular diseases. A novel Predator Crow Optimization and Deep Neural Network framework predicted cardiovascular diseases with 96.67% accuracy, outperforming existing classification models.
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