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

BMC Medical Research Methodology

Research

Anoshirvan Kazemnejad¹  Kimiya Gohari¹  Ali Sheidaei²  Farzad Eskandari³  Samaneh Saberi⁴  Marjan Mohammadi⁴  Maryam Esmaieli⁴ 

[1] Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran;Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran;Department of Statistics, Allameh Tabataba’i University, Tehran, Iran;HPGC Research Group, Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran;

关键词: Naïve Bayesian classifier;    Bayesian latent class analysis;    Gibbs sampling;    Expectation maximization algorithm;    Gastric cancer;   

DOI  :  10.1186/s12874-023-02013-4

 received in 2022-11-05, accepted in 2023-08-08,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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 studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure expansion.In this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the classifier.MethodsBoth Expectation-Maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold cross-validation.ResultsThe presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, respectively.ConclusionsWhen considering the modification of the NB classifier, incorporating a latent component into the model offers numerous advantages, particularly within medical and health-related contexts. By doing so, the researchers can bypass the extensive search algorithm and structure learning required in the local learning and structure extension approach. The inclusion of latent class variables allows for the integration of all attributes during model construction. Consequently, the NB-BLCA model serves as a suitable alternative to conventional NB classifiers when the assumption of independence is violated, especially in domains pertaining to health and medicine.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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