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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Bioengineering (Basel) Jg. 11; H. 8; S. 822
Hauptverfasser:	Talaat, Fatma M., Elnaggar, Ahmed R., Shaban, Warda M., Shehata, Mohamed, Elhosseini, Mostafa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.08.2024 MDPI
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!