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Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence
BACKGROUND: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of W...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
West Asia Organization for Cancer Prevention
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5747394/ https://www.ncbi.nlm.nih.gov/pubmed/29072396 http://dx.doi.org/10.22034/APJCP.2017.18.10.2709 |
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author | Ogunsakin, Ropo Ebenezer Siaka, Lougue |
author_facet | Ogunsakin, Ropo Ebenezer Siaka, Lougue |
author_sort | Ogunsakin, Ropo Ebenezer |
collection | PubMed |
description | BACKGROUND: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. MATERIALS AND METHODS: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. RESULTS: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. CONCLUSION: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer. |
format | Online Article Text |
id | pubmed-5747394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | West Asia Organization for Cancer Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-57473942018-02-21 Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence Ogunsakin, Ropo Ebenezer Siaka, Lougue Asian Pac J Cancer Prev Research Article BACKGROUND: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. MATERIALS AND METHODS: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. RESULTS: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. CONCLUSION: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer. West Asia Organization for Cancer Prevention 2017 /pmc/articles/PMC5747394/ /pubmed/29072396 http://dx.doi.org/10.22034/APJCP.2017.18.10.2709 Text en Copyright: © Asian Pacific Journal of Cancer Prevention http://creativecommons.org/licenses/BY-SA/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License |
spellingShingle | Research Article Ogunsakin, Ropo Ebenezer Siaka, Lougue Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title | Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title_full | Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title_fullStr | Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title_full_unstemmed | Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title_short | Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence |
title_sort | bayesian inference on malignant breast cancer in nigeria: a diagnosis of mcmc convergence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5747394/ https://www.ncbi.nlm.nih.gov/pubmed/29072396 http://dx.doi.org/10.22034/APJCP.2017.18.10.2709 |
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