USING MULTINOMIAL LOGISTIC REGRESSION TO IDENTIFY FACTORS AFFECTING PLATELET

SHERZAD MOHAMMAD  AJEEL; JIYAN ALI  HAJI; BANAZ HAMZA  JAHWAR

doi:10.26682/sjuod.2023.26.2.6

SHERZAD MOHAMMAD AJEEL Dept. of Mathematics, College of Science, University of Duhok, Kurdistan Region-Iraq
JIYAN ALI HAJI Dept. of Medical Laboratory, College of Health Science, University of Cihan / Duhok, Kurdistan Region-Iraq
BANAZ HAMZA JAHWAR Dept. of Mathematics, College of Science, University of Duhok, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/sjuod.2023.26.2.6

Keywords: Multinomial logistic regression model; Odds ratio; categorical data analysis; maximum likelihood method; Binary variable

Abstract

The objective of this work is to find an application for the Multinomial Logistic Regression (MLR) model, which is one of the essential methods for categorical data analysis. The focus of this paradigm is a single nominal or ordinal response variable with more than two categories. Data analysis using this method has been conducted in various disciplines, including health, social sciences, behavioral studies, and education.

To practically identify the model's application, we utilized real data from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) patients. Five explanatory variables were included in creating the main multinomial logistic regression model. A series of statistical tests were performed to confirm the model's suitability for the data. Furthermore, the model was put to the test by randomly selecting two observations from the data to forecast their categorization based on the explanatory variable values used.

Our conclusion is that the multinomial logistic regression model enables us to effectively characterize the link between the explanatory variable set and the response variable, identify the impact of each variable, and forecast the classification of any given case

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References

Y. R. Gel, V. Lyubchic, & L.L. Ramirez, Fast Patchwork Bootstrap for Quantifying Estimation Uncertainties in Sparse Random Networks, USA, ( 2016).
C.R. Bilder, T.M. Loughin, Analysis of Categorical Data with R. Boca Raton, FL: Chapman & Hall/CRC, (2015).
M. H. Joseph, Practical Guide to Logistic Regression, Taylor & Francis Group, LLC, (2015).
Y. Liu, On Goodness of Fit of Logistic Regression Model, PhD. Thesis, Kansas State University, Manhattan, Kansas. (2007).
A. Abbasi, J. Altmann, L. Hossain, Identifying The Effects of Co-Authorship Networks on The Performance of Scholars: A Correlation and Regression Analysis of Performance Measures and Social Network Analysis Measures, Volume 5, Issue 4, Pages 594-607, ( 2011).
I. Fathy,Use of information criteria and detection model methods to select the best linear regression model with application on thalassemia children in Mosul , journal of education and science 25(2):189-200, June (2012).
Y. Gel, V. Lyubchich, L. Ramirez, Sparse Random Networks, Scientific Reports journal, (2016).
C.J. Peng, K.L. Lee & G.M. Ingersoll, An Introduction to Logistic Regression Analysis and Reporting, The Journal of Educational Research, 96(1), 3-14, (2002).
S. Aggarwal, S. Gollapudi, S. Gupta, Increased TNF-Alpha-Induced Apoptosis in Lymphocytes from Aged Humans: Changes in TNF-Alpha Receptor Expression and Activation of caspases. J Immunol, 162, 2154-2161, (1999).
D.Kleinbaum, M. Klein, Logistic Regression(Statistics for Biology and Health) (3rd ed.), New York, NY: Springer-Verlag New York Inc, (2010).
A. Assinger, Platelets and Infection - An Emerging Role of Platelets in Viral Infection, Front Immunol, 5: 649, (2014).
A. Agresti. An Introduction to Categorical Data Analysis, New York, NY: Wiley & Sons, (1996).
H. Go , H. Ohto, K. Nollet, N. Kashiwabara, M. Chishiki, M. Hoshino, K. Ogasawara, Y. Kawasaki, N. Momoi, M. Hosoya , Perinatal Factors Affecting Platelet Parameters in Late Preterm and Term Neonates, Plos One, (2020).
K. Skok, E. Stelzl, M. Trauner, H .H. Kessler, S.F. Lax, Post-Mortem Viral Dynamics and Tropism in COVID-19 Patients in Correlation with Organ Damage. Virchows Arch., 478, 343–353, ( 2021).
D. Giannis, I.A. Ziogas, P. Gianni, Coagulation Disorders in Coronavirus Infected Patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J. Clin. Virol., 127, 104362, (2020).
Y. R. Gel, V. Lyubchic, & L.L. Ramirez, Fast Patchwork Bootstrap for Quantifying Estimation Uncertainties in Sparse Random Networks, USA, ( 2016).
Y. Zhu, J. Zhang, Y. Li, F. Liu, Q. Zhou, Z. Peng, D. Cox, Association between Thrombocytopenia and 180-Day Prognosis of COVID-19 Patients in Intensivecare Units: a Two-Center Observational Study, Plos ONE 16 (3) e0248671, (2021) .
A. Le, L. Biard, M. Vautier, H. Bugaut, A. Mekinian, G. Maalouf, M. Vieira, A. Marcelin, M. Rosenzwajg, D. Klatzmann, JC. Corvol, O. Paccoud, A. Carillion, JE. Salem, P. Cacoub, Y. Boulaftali, D. Saadoun, Neutrophil-Platelet and Monocyte-Platelet Aggregates in COVID-19 Patients, Thromb. Haemost, 120 (12) 1733–1735, (2020).
A. Zarbock, K. Singbartl, K. Ley, Complete Reversal of Acid-Induced Acute Lung Injury By Blocking Of Platelet-Neutrophil Aggregation,J. Clin. Investig, 116 (12) 3211–3219,(2006).
M. Gunaldi, S. Goksu, D. Erdem, S. Gunduz, Y. Okuturlar, E. Tiken, S. Kahraman, Y. Inan, M. Yildirim, Prognostic Impact of Platelet/Lymphocyte and Neutrophil/Lymphocyte Ratios in Patients With Gastric Cancer: A Multicenter Study, Int J ClinExp Med, 8, 5937-5942, (2015).
H. Xu, L. Zhong, J. Deng, J. Peng, H. Dan, X. Zeng, Q. Chen, High Expression Of ACE2 Receptor of 2019-Ncov on the Epithelial Cells of Oral Mucosa. Int J Oral Sci; 12: 1-5, (2020).
T. Li, H. Lu, W. Zhang, Clinical Observation and Management of COVID-19 Patients. Emerg Microbes Infect; 9: 687-690, (2020).
V. Saraswat, Indian Society of Gastroenterology, Indian J Gastroenterol, 32, 1-132. 15, (2013).
S. M. Ajeel, H. Hashem, Comparison Some Robust Regularization Methods in Linear Regression via Simulation Study , Academic Journal of Nawroz University (AJNU), Vol.9, No.2, Jan, (2020).
J.F. Quispe-Pari, J.A. Gonzales-Zamora, J. Munive-Dionisio, C. Castro-Contreras, A. Villar-Astete, C. Kong-Paravicino, P. Vilcapoma-Balbin & J. Hurtado-Alegre, Mean Platelet Volume as a Predictor of COVID-19 Severity: A Prospective Cohort Study in the Highlands of Peru. Diseases, 10(2), p.22, (2022).
A. Assinger, Platelets and Infection - An Emerging Role of Platelets in Viral Infection, Front Immunol, 5: 649, (2014).
R. Angioni et al., Age-Severity Matched Cytokine Profiling Reveals Specific Signatures in Covid-19 Patients, Cell Death & Disease, vol. 11, no. 11, Available: 10.1038/s41419-020-03151-z, (2020).
A. Agresti, An Introduction to Categorical Data Analysis, 2nd edn , John Wiley & Sons, Inc., Hoboken, New Jersey, (2007).
D. Garson, Logistic Regression with SPSS, North Carolina State University, Public administration Program, (2009)