Abstract
Classifying using Multinomial Logistic Regression is one of the techniques used in the statistical model. Binomial logistic regression is a form of regression that is used when the dependent is a dichotomy and the independents are continuous variables, categorical variables, or both. Multinomial Logistic Regression Model is used to handle the case of dependent variables with more classes and suitable for non-linear data by using Maximum Likelihood Estimation and Wald Statistics. This experiment aims to predict the Thyroid disease Dataset by using the Multinomial Logistic Regression model. In this experiment, SPSS software is used to run the Multinomial Logistic Regression by Thyroid disease data supplied by Randolf Werner, which was adopted from the UCI Repository of Machine Learning Database.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Becker, H., Xiao, B., Naaman, M., & Gravano, L. (2010). Exploiting social links for event identification in social media.
Callan, J. (2002). Distributed information retrieval. In Advances in information retrieval (pp. 127–150). Springer.
Larson, R. R. (2002). A logistic regression approach to distributed IR. January 2002, p. 399. https://doi.org/10.1145/564376.564463.
Ng, K.-C., & Lai, S.-W. (2004). Application of anthropometric indices in childhood obesity. Southern Medical Journal, 97(6), 566–571.
Powell, A. L., & French, J. C. (2003). Comparing the performance of collection selection algorithms. ACM Transaction on Information Systems, 21(4), 412–456.
Qian, J. (2003). Application of logistic regression in analysis of e-rater data. Educational Testing Service. ETS MS.
Steyerberg, J. D. F., Eijkemans, E. W., & Habbema, M. J. C. (2002). Application of shrinkage techniques in logistic regression analysis: A case study. Statistica Neerlandica, 55(1), 76–88.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Darawsheh, S.R., Al-Shaar, A.S., Haziemeh, F.A., Alshurideh, M.T. (2023). Classification Thyroid Disease Using Multinomial Logistic Regressions (LR). In: Alshurideh, M., Al Kurdi , B.H., Masa’deh, R., Alzoubi , H.M., Salloum, S. (eds) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, vol 1056. Springer, Cham. https://doi.org/10.1007/978-3-031-12382-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-12382-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12381-8
Online ISBN: 978-3-031-12382-5
eBook Packages: EngineeringEngineering (R0)