A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients

A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients

Abstract Background The Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various stud...

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Detalles Bibliográficos
Main Authors: Kimiya Gohari, Anoshirvan Kazemnejad, Marjan Mohammadi, Farzad Eskandari, Samaneh Saberi, Maryam Esmaieli, Ali Sheidaei
Formato: Artigo
Idioma:English
Publicado: BMC 2023-08-01
Series:BMC Medical Research Methodology
Subjects:
Naïve Bayesian classifier
Bayesian latent class analysis
Gibbs sampling
Expectation maximization algorithm
Gastric cancer
Acceso en liña:https://doi.org/10.1186/s12874-023-02013-4

Internet

https://doi.org/10.1186/s12874-023-02013-4

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