A Modified Multivariate Bayesian Logistic Model with Application to Health Datasets

Autor: Azza Mustafa Abd Al Kader Al Kusaem
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Ibn Al-Haitham Journal for Pure and Applied Sciences, Vol 37, Iss 4 (2024)
Druh dokumentu: article
ISSN: 1609-4042
2521-3407
DOI: 10.30526/37.4.3245
Popis: The application of Bayesian strategies for binary logistic estimation is demonstrated in this article. A modified method of the Bayesian logistic model using the Metropolis-Hasting algorithm is derived and applied to three simulation data sets. We compared the new model with existing classification methods: support vector machine, artificial neural network and regular logistic model. The modified model was used to classify the heart disease dataset. The data came from a database intended for UCI Data Science (https://www.kaggle.com). The clarification accuracy and the time required are checked and compared with other standard methods. It has been shown that the presented model has the best accuracy and efficiency compared to the different classification methods. All calculations were performed with the program R version 4.2.2.
Databáze: Directory of Open Access Journals