CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, alth...

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Bibliographic Details
Published in:	Bioengineering (Basel) Vol. 11; no. 8; p. 822
Main Authors:	Talaat, Fatma M., Elnaggar, Ahmed R., Shaban, Warda M., Shehata, Mohamed, Elhosseini, Mostafa
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.08.2024 MDPI
Subjects:	active learning Artificial intelligence Cardiovascular disease Cardiovascular diseases cardiovascular diseases (CVDs) Decision making Explainable artificial intelligence Fatalities Imaging techniques Integration Learning Medical research Medicine, Experimental Mortality Patients Predictions Prognosis Risk assessment risk prediction cardiovascular diseases (CVDs) active learning eXplainable artificial intelligence risk prediction
ISSN:	2306-5354, 2306-5354
Online Access:	Get full text
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