On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria

Nicholas Pindar Dibal; Christopher Akas Abraham

doi:doi:10.11648/j.ajtas.20200903.14

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On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria

Nicholas Pindar Dibal, Christopher Akas Abraham

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3)

Received: 16 April 2020 Accepted: 3 May 2020 Published: 18 May 2020

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3)
DOI	10.11648/j.ajtas.20200903.14
Page(s)	53-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Discriminant Analysis, Classification, Diabetes, Fisher’s LDF

References

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[2]	Alayande, S. A. and Bashi, K. A. (2015). An Overview and Application of Discriminant Analysis in Data Analysis. IOSR Journal of Mathematics (IOSR-JM). 11 (1), 12-15. DOI: 10.9790/5728-11151215.
[3]	AlKubaisi, M., Aziz, W. A., George, S. and Al-Tarawneh, K, (2019). Multivariate Discriminant Analysis Managing Staff Appraisal Case Study. Academy of Strategic Management Journal. 18 (5), Online ISSN: 1939-6104
[4]	Antonogeorgos, G., Demosthenes, B. P., Kostas, N. P. and Anastasia, T. (2009). Logistic Regression and Linear Discriminant Analyses in Evaluating Factors Associated with Asthma Prevalence among 10- to 12-Years-Old Children: Divergence and Similarity of the Two Statistical Methods. International Journal of Pediatrics. doi: 10.1155/2009/952042
[5]	Bhuyan, K. C. (2005). Multivariate Analysis and its Application. Department of Statistics, Garyounis University, Libya. New Central Book Agency (P) Ltd.
[6]	Cai, D., He, X. and Han, J. (2008). Srda: An Efficient Algorithm for Large-scale Discriminant Analysis. Knowledge and Data Engineering, 20 (1): 1–12.
[7]	Clemmensen, L. K. H. (2013). On Discriminant Analysis Techniques and Correlation Structures in High Dimensions. Kgs. Lyngby: Technical University of Denmark. Technical Report-2013, No. 04
[8]	Erimafa, J. T. (2009), Application of Discriminant Analysis to Predict the Class of Degree for Graduating Students in a University System. International journal of physical science, 4 (1), 16 – 21.
[9]	Fisher, R. A. (1938). The Statistical Utilization of Multiple Measurements. Ann. Eng. Lond. 7, 179-88.
[10]	Gebru, T. G. (2018). Sparse Linear Discriminant Analysis with more Variables than Observations. Ph. D. Thesis. The Open University
[11]	Hafez, E. I., Abdel-Fatah, E. M., Abdel-Nabi, S. M. and Zeidan, A. S. A. (2015). Discriminant Analysis in View of Statistical and Operations Research Techniques. Journal of Multidisciplinary Engineering Science and Technology (JMEST). 2 (11), 3039-3047
[12]	Mbanasor J. A. and Nto, P. O. O. (2008). Discriminant Analysis of Livestock Farmers’ Credit Worthiness Potentials under Rural Banking Scheme in Abia State, Nigeria. Nigerian Agricultural Journal, 39 (1), 1-7.
[13]	Micheal, A. B. (2014). Application of Discrimination and Classification on Diabetes Mellitus Data. International Journal of Applied Science and Technology, 4 (6). 292-298
[14]	Schlegel, A. (2018). Linear Discriminant Analysis for the Classification of Two Groupshttps://aaronschlegel.me/linear-discriminant-analysis-classification-two-groups.html

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APA Style

Nicholas Pindar Dibal, Christopher Akas Abraham. (2020). 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, 9(3), 53-56. https://doi.org/10.11648/j.ajtas.20200903.14

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ACS Style

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. Am. J. Theor. Appl. Stat. 2020, 9(3), 53-56. doi: 10.11648/j.ajtas.20200903.14

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AMA Style

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. Am J Theor Appl Stat. 2020;9(3):53-56. doi: 10.11648/j.ajtas.20200903.14

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@article{10.11648/j.ajtas.20200903.14,
  author = {Nicholas Pindar Dibal and Christopher Akas Abraham},
  title = {On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {3},
  pages = {53-56},
  doi = {10.11648/j.ajtas.20200903.14},
  url = {https://doi.org/10.11648/j.ajtas.20200903.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200903.14},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria
AU  - Nicholas Pindar Dibal
AU  - Christopher Akas Abraham
Y1  - 2020/05/18
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200903.14
DO  - 10.11648/j.ajtas.20200903.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
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PB  - Science Publishing Group
SN  - 2326-9006
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AB  - 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.
VL  - 9
IS  - 3
ER  -

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Author Information

Nicholas Pindar Dibal

Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Christopher Akas Abraham

Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria

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APA Style

Nicholas Pindar Dibal, Christopher Akas Abraham. (2020). 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, 9(3), 53-56. https://doi.org/10.11648/j.ajtas.20200903.14

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ACS Style

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. Am. J. Theor. Appl. Stat. 2020, 9(3), 53-56. doi: 10.11648/j.ajtas.20200903.14

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AMA Style

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. Am J Theor Appl Stat. 2020;9(3):53-56. doi: 10.11648/j.ajtas.20200903.14

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@article{10.11648/j.ajtas.20200903.14,
  author = {Nicholas Pindar Dibal and Christopher Akas Abraham},
  title = {On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {3},
  pages = {53-56},
  doi = {10.11648/j.ajtas.20200903.14},
  url = {https://doi.org/10.11648/j.ajtas.20200903.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200903.14},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - On the Application of Linear Discriminant Function to Evaluate Data on Diabetic Patients at the University of Port Harcourt Teaching Hospital, Rivers, Nigeria
AU  - Nicholas Pindar Dibal
AU  - Christopher Akas Abraham
Y1  - 2020/05/18
PY  - 2020
N1  - https://doi.org/10.11648/j.ajtas.20200903.14
DO  - 10.11648/j.ajtas.20200903.14
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 53
EP  - 56
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20200903.14
AB  - 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.
VL  - 9
IS  - 3
ER  -

Copy | Download