A stacking ensemble model for predicting the occurrence of carotid atherosclerosis

Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related mark...

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Veröffentlicht in:	Frontiers in endocrinology (Lausanne) Jg. 15; S. 1390352
Hauptverfasser:	Zhang, Xiaoshuai, Tang, Chuanping, Wang, Shuohuan, Liu, Wei, Yang, Wangxuan, Wang, Di, Wang, Qinghuan, Tang, Fang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media S.A 23.07.2024
Schlagworte:	Aged Algorithms Carotid Artery Diseases - epidemiology carotid atherosclerosis Cohort Studies endocrine-related markers Endocrinology Female Humans Machine Learning Male Middle Aged prediction Prognosis Risk Assessment - methods Risk Factors stacking Support Vector Machine carotid atherosclerosis prediction endocrine-related markers machine learning stacking
ISSN:	1664-2392, 1664-2392
Online-Zugang:	Volltext
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