Abstract
Coronary atherosclerotic heart disease is a common and high-risk disease in clinical cardiology. The leading factor of this kind of heart disease is the atherosclerotic lesion of arteria coronary, which can lead to narrow or choke of the vascellum cavity and musculus cardiacus ischemia, anoxia or thanatosis. In this research, a clinical prediction model through artificial intelligence neural network is built, for instance, K-Nearest Neighbor Classifier, Nonlinear SVM, Random Forest, Gradient Boost, XG Boost, Ridge and so on to analyze the influence of each prediction variable on Coronary atherosclerotic heart. It can be observed that using the K-Nearest Neighbor Classifier algorithm the prediction accuracy of breast cancer reaches 90.16%.
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.
