A Modified Multivariate Bayesian Logistic Model with Application to Health Datasets

Azza Mustafa Abd Al Kader Al Kusaem

doi:10.30526/37.4.3245

Authors

Azza Mustafa Abd Al Kader Al Kusaem Department of Business Administration, College of Administration and Economics, University of Mosul, Mosul, Iraq. https://orcid.org/0000-0003-2322-8991

DOI:

https://doi.org/10.30526/37.4.3245

Abstract

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.

References

Zhang, L.; Dong, L.; Cheng, S.; Li, W.; Wang, B.; Liu, H.; Chen, K. Efficient reliability assessment method for bridges based on Markov Chain Monte Carlo (MCMC) with Metropolis-Hasting Algorithm (MHA). In IOP Conference Series: Earth and Environmental Science 2020, 580(1), 012030. https://doi.org/10.1088/1755-1315/580/1/012030

Mahdi GJ, Kalaf BA, Khaleel MA. Enhanced Supervised Principal Component Analysis for Cancer Classification. Iraqi Journal of Science 2021, 62(4), 1321-1333. https://doi.org/10.24996/ijs.2021.62.4.28

Kaji T; Ročková V. Metropolis–Hastings via Classification. Journal of the American Statistical Association. 2023, 118(544), 2533-2547. https://doi.org/10.1080/01621459.2022.2060836

Sur, P.; Candès, EJ. A modern maximum-likelihood theory for high-dimensional logistic regression. Proceedings of the National Academy of Sciences 2019, 116(29), 14516-25. https://doi.org/10.1073/pnas.1907936116

Belenguer-Llorens, A.; Sevilla-Salcedo, C.; Desco, M.; Soto-Montenegro, M.L.; Gómez-Verdejo, V. A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data. Applied Science 2022, 12(5), 2571. https://doi.org/10.3390/app12052571

Jeune, W.; Francelino, M.R.; Souza, E.D.; Fernandes Filho, E.I.; Rocha, G.C. Multinomial logistic regression and random forest classifiers in digital mapping of soil classes in western Haiti. Revista Brasileira de Ciência do Solo 2018, 42, e0170133. https://doi.org/10.1590/18069657rbcs20170133

Liu, Y.; Bi, J.W.; Fan, Z.P. A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Information Sciences. 2017, 394, 38-52. https://doi.org/10.1016/j.ins.2017.02.016

Dwivedi, AK. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Computing and Applications 2018, 29(12), 1545-54. https://doi.org/10.1007/s00521-016-2701-1

Algamal, Z. An efficient gene selection method for high-dimensional microarray data based on sparse logistic regression. Electronic Journal of Applied Statistical Analysis 2017, 10(1), 242-56. https://doi.org/10.1285/i20705948v10n1p242

Ding, Y.J.; Wang, Z.C.; Chen, G.; Ren, W.X.; Xin, Y. Markov Chain Monte Carlo-based Bayesian method for nonlinear stochastic model updating. Journal of Sound and Vibration 2022, 520, 116595. https://doi.org/10.1016/j.jsv.2021.116595

Guo, Y.; Li, Z.; Liu, P.; Wu, Y. Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model. Accident Analysis & Prevention 2019, 128, 164-74. https://doi.org/10.1016/j.aap.2019.04.001

Serrano, BM.; González-Cancelas, N.; Soler-Flores, F. Camarero-Orive, A. Classification and prediction of port variables using Bayesian Networks. Transport Policy 2018, 67, 57-66. https://doi.org/10.1016/j.tranpol.2017.03.016

Algamal, ZY.; Alhamzawi, R.; Ali, HT. Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression. Computers in biology and medicine 2018, 97, 145-52. https://doi.org/10.1016/j.compbiomed.2018.04.002

Mahdi GJM, Mohammed NJ, Al-Sharea ZI. Regression shrinkage and selection variables via an adaptive elastic net model. In Journal of Physics: Conference Series 2021, 1879(3), 032014. https://doi.org/10.1088/1742-6596/1879/3/032014

Rai A, Chatterjee S, Nag A. A novel hybrid machine learning approach for reliable bridge condition assessment. Structure and Infrastructure Engineering 2021, 17(4), 489-502. https://doi.org/10.1080/15732479.2020.1838954

Wu, C., Jin, Z.; Sun, J. Structural reliability assessment using adaptive surrogate models and Markov Chain Monte Carlo simulation. Engineering Structures 2020, 222, 111088. https://doi.org/10.1016/j.engstruct.2020.111088

Liang QQ, Li Y, Li AQ, Li J. Application of Bayesian network and support vector machine to bridge safety assessment. Structural Safety 2019, 78, 52-62. https://doi.org/10.1016/j.strusafe.2018.12.003

Huang, H.; Han, Y.; Sun, J. Reliability-based design optimization of steel-concrete composite bridges considering load and resistance factors. Journal of Bridge Engineering 2020, 25(3), 04019137. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001513

Wang, S.; Li, Q.; Ma, J. A Bayesian approach to reliability assessment for corroded steel bridges using structural health monitoring data. Journal of Constructional Steel Research 2021, 180, 106618. https://doi.org/10.1016/j.jcsr.2021.106618

Lee, K.; Yang. M.; Suh, MW. Reliability analysis of bridges using a stochastic finite element method and surrogate models. Computers and Structures 2020, 238, 106306. https://doi.org/10.1016/j.compstruc.2020.106306

Wu, Y.; Zhang, S.; Cai, CS. Structural reliability analysis of long-span bridges under heavy traffic load and strong wind. Journal of Structual Engineering 2020, 146(9), 04020182. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002724

Li, S.; Liang, X.; Zhang, Z. A novel hybrid method for reliability assessment of existing bridges under heavy traffic loads. Advances in Mechanical Engineering 2021, 13(2), 1687814021993540. https://doi.org/10.1177/1687814021993540

Zhang, Q.; Liu, H.; Xu, Z. Reliability-based maintenance optimization for deteriorating bridges using a multi-objective approach. Automation Construction 2020, 117, 103258. https://doi.org/10.1016/j.autcon.2020.103258

Yuen, KK.; Wong, SS.; Yeung, TY. Reliability assessment of bridge structures using non-parametric Bayesian method with Gaussian process. Reliability Engineering & System Safety 2021, 206, 107279. https://doi.org/10.1016/j.ress.2020.107279