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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Vydáno v:	Bioengineering (Basel) Ročník 11; číslo 8; s. 822
Hlavní autoři:	Talaat, Fatma M., Elnaggar, Ahmed R., Shaban, Warda M., Shehata, Mohamed, Elhosseini, Mostafa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.08.2024 MDPI
Témata:	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
On-line přístup:	Získat plný text
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