Abstract
Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices' standard deviation and comparing the results against its parents (RSD-ANN). The performance of RSD-ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross-validation or holdout method.
Highlights
Cardiovascular disease, or CVD, refers to various heart disorders, including structural heart abnormalities and blood vessel blockages
Without classifying and predicting CVD, these models’ performance in classifying and predicting cardiovascular disease declines significantly. After developing such a model, the proper evaluation must be validated through a strong channel, such as using a medical and research institute for heart disease detection and prediction. is study proposed a new predictive model for early heart disease detection, in which weight decay is used to shrink the influence of each data point in the weight matrix
We present the design and implementation of a regularizer based on the Relative Standard Deviation for the Artificial Neural Network (RSD-Artificial Neural Networks (ANNs)) system
Summary
Cardiovascular disease, or CVD, refers to various heart disorders, including structural heart abnormalities and blood vessel blockages. Just like humans, we are not infallible, and our decisions could result in either a life-saving or a tragic outcome In these critical situations, machine learning techniques have emerged. Without classifying and predicting CVD, these models’ performance in classifying and predicting cardiovascular disease declines significantly. After developing such a model, the proper evaluation must be validated through a strong channel, such as using a medical and research institute for heart disease detection and prediction. Is study proposed a new predictive model for early heart disease detection, in which weight decay is used to shrink the influence of each data point in the weight matrix. Is paper is organized as follows: Section 2 discusses the methods employed in the detection of heart diseases.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
