Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques

Abstract Background This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Gamal Saad Mohamed Khamis, Sultan Munadi Alanazi
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: BMC 2023-05-01
Sarja:BMC Medical Informatics and Decision Making
Aiheet:
Principal component analysis (PCA)
Latent class analysis (LCA)
Bayesian information criteria (BIC)
Cardiovascular disease (CVD)
And risk factors
Linkit:https://doi.org/10.1186/s12911-023-02179-3

Internet

https://doi.org/10.1186/s12911-023-02179-3

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