Forecasting Coronary Heart Disease Risk with a 2-Step Hybrid Ensemble Learning Method and Forward Feature Selection Algorithm

Detecting cardiovascular irregularities in a timely manner is crucial for preventing any fatal risks. This research aims to devise an efficient forecasting algorithm for the timely prognosis of Coronary Heart Disease (CHD). The study includes a diverse sample of individuals from Framingham, Massachu...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Patra, Sushree Chinmayee, Uma Maheswari, B., Pati, Peeta Basa
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Cardiovascular disease Classifiers Correlation coefficients Diseases Ensemble learning F1 score Feature importance Feature optimization Feature selection Feature Weighted Meta-Models Forecasting Heart Heart diseases Hybrid Voting Model Machine learning Mathematical models MCC Medical services Misclassification Ratio Model accuracy Predictive models Radio frequency Random forests Risk Support vector machines Time complexity
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!