On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 3, May 2020, Pages: 53-56
Received: Apr. 16, 2020;
Accepted: May 3, 2020;
Published: May 18, 2020
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Nicholas Pindar Dibal, Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Christopher Akas Abraham, Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Many real life events involves several interacting variables, hence multivariate statistical tool is necessary for appropriate analysis and interpretation. Discriminant analysis (DA) is one of the commonly used multivariate method in various fields of study including education, finance, environment, medicine etc., where complex data analysis and interpretation is required. This paper demonstrates and illustrate approaches in presenting how the discriminant analysis can be carried out on 335 (40 diabetics and 295 non-diabetic) patients and how the output can be interpreted using the Fisher’s linear Discriminant function (FLDF). The performance of FLDF was adjudged based on the percentage of correct reclassification of the original observation to yield the discriminant scores from the functions. Up to 65.4% correct classification was achieved, and similarly 62.7% percent of the cross-validated grouped cases were correctly classified into either being a Diabetic or non-diabetic patient. Patient’s age and gender were found to be the two most important contributing variables in classifying a patient between the two groups.
Nicholas Pindar Dibal,
Christopher Akas Abraham,
On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria, American Journal of Theoretical and Applied Statistics.
Vol. 9, No. 3,
2020, pp. 53-56.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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