Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital

Ojo Timothy Ayodele; Alabi Olatayo Olusegun; Oyegoke Adiat Odunayo; Ayinde Liasu Adekunle; Ogunwole Bolawa Adijat

doi:doi:10.11648/j.ijdst.20200602.11

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Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital

Ojo Timothy Ayodele, Alabi Olatayo Olusegun, Oyegoke Adiat Odunayo, Ayinde Liasu Adekunle, Ogunwole Bolawa Adijat

Published in International Journal on Data Science and Technology (Volume 6, Issue 2)

Received: 25 April 2020 Accepted: 21 May 2020 Published: 10 August 2020

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Abstract

In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x₁), Urea (x₂), temperature (x₃), Fasting blood sugar (x₄), Body mass index (x₅), and marital status (x₆). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x₁+0.008 x₂-0.897 x₃-0.021 x₄-0.017 x₅-2.872 x₆. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.

Published in	International Journal on Data Science and Technology (Volume 6, Issue 2)
DOI	10.11648/j.ijdst.20200602.11
Page(s)	53-55
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

Dicriminant, Classification, Multivariate, Misclassification, Diabetic Patient

References

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[2]	Deswal B. S., Singh J. V., Kumar D.- A Study of Risk Factors for Low Birth Weight, Indian J. Community Med. 24, 2008: 127-131.
[3]	Philip, J. Disala, Clinical Gynaecologicon. cology. (4th Edition). Mosby year book Inc. 1995.
[4]	Geoffrey. V. P Chamberline, Gynaecology by Ten Teachers. (16th Edition). ELBS. 1988.
[5]	Daniel B. Rowe, Multivariate Bayesian Statistics. Chapman and Hall/CRC, 2003.
[6]	Anderson, T. W. An Introduction toMultivariate Statistical Analysis. New York: Wiley. 1984.
[7]	Barnett, V. (ed.), Interpreting Multivariate Data. Wiley. 1981.
[8]	David, W. Stockburger, Multivariate Statistics: Concepts, Models and Applications. 1998.
[9]	Nwobi and Nduka. Statistical Notes and Tables for Research, Second Edition. Alphabet Nigeria Publishers. 2003.
[10]	Nwachukwu V. O, Principles of Statistical Inference. Second Edition. Zelon Enterprises. 2006.
[11]	Everitt, B. S & Dunn, G, Applied Multivariate Statistical Analysis. (2nd Edition). Arnold. 2001.
[12]	Alvin B. Rowe, “Methods of Multivariate Analysis, second edition” Wiley –Interscience, 2003.
[13]	Adejumo, A. O and Onyenekwe, C. E (2014) A Study of Classification and Discriminant Analysis of Infants at Birth in Nigeria. IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 9, Issue 4 (Jan. 2014), PP 22-26 www.iosrjournals.
[14]	Andrew E. Uloko, Baba M. Musa, Mansur A. Ramalan, Ibrahim D. Gezawa, Fabian H. Puepet, Ayekame T. Uloko, Musa M. Borodo, and Kabiru B. Sad (2018) Prevalence and Risk Factors for Diabetes Mellitus in Nigeria: A Systematic Review and Meta-Analysis BMJ Open Diabetes Res Care.; 2 (4) 127-130.

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APA Style

Ojo Timothy Ayodele, Alabi Olatayo Olusegun, Oyegoke Adiat Odunayo, Ayinde Liasu Adekunle, Ogunwole Bolawa Adijat. (2020). Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. International Journal on Data Science and Technology, 6(2), 53-55. https://doi.org/10.11648/j.ijdst.20200602.11

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ACS Style

Ojo Timothy Ayodele; Alabi Olatayo Olusegun; Oyegoke Adiat Odunayo; Ayinde Liasu Adekunle; Ogunwole Bolawa Adijat. Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. Int. J. Data Sci. Technol. 2020, 6(2), 53-55. doi: 10.11648/j.ijdst.20200602.11

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AMA Style

Ojo Timothy Ayodele, Alabi Olatayo Olusegun, Oyegoke Adiat Odunayo, Ayinde Liasu Adekunle, Ogunwole Bolawa Adijat. Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. Int J Data Sci Technol. 2020;6(2):53-55. doi: 10.11648/j.ijdst.20200602.11

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@article{10.11648/j.ijdst.20200602.11,
  author = {Ojo Timothy Ayodele and Alabi Olatayo Olusegun and Oyegoke Adiat Odunayo and Ayinde Liasu Adekunle and Ogunwole Bolawa Adijat},
  title = {Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital},
  journal = {International Journal on Data Science and Technology},
  volume = {6},
  number = {2},
  pages = {53-55},
  doi = {10.11648/j.ijdst.20200602.11},
  url = {https://doi.org/10.11648/j.ijdst.20200602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20200602.11},
  abstract = {In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x1), Urea (x2), temperature (x3), Fasting blood sugar (x4), Body mass index (x5), and marital status (x6). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x1+0.008 x2-0.897 x3-0.021 x4-0.017 x5-2.872 x6. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.},
 year = {2020}
}

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TY  - JOUR
T1  - Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital
AU  - Ojo Timothy Ayodele
AU  - Alabi Olatayo Olusegun
AU  - Oyegoke Adiat Odunayo
AU  - Ayinde Liasu Adekunle
AU  - Ogunwole Bolawa Adijat
Y1  - 2020/08/10
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdst.20200602.11
DO  - 10.11648/j.ijdst.20200602.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 53
EP  - 55
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20200602.11
AB  - In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x1), Urea (x2), temperature (x3), Fasting blood sugar (x4), Body mass index (x5), and marital status (x6). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x1+0.008 x2-0.897 x3-0.021 x4-0.017 x5-2.872 x6. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.
VL  - 6
IS  - 2
ER  -

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Author Information

Ojo Timothy Ayodele

Lautech Teaching Hospital, Osogbo, Nigeria
Alabi Olatayo Olusegun

Department of Statistics, Federal University of Technology, Akure, Nigeria
Oyegoke Adiat Odunayo

Department of statistics, Osun State Polytechnic, Iree, Nigeria
Ayinde Liasu Adekunle

Department of statistics, Osun State Polytechnic, Iree, Nigeria
Ogunwole Bolawa Adijat

Department of statistics, Osun State Polytechnic, Iree, Nigeria

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APA Style

Ojo Timothy Ayodele, Alabi Olatayo Olusegun, Oyegoke Adiat Odunayo, Ayinde Liasu Adekunle, Ogunwole Bolawa Adijat. (2020). Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. International Journal on Data Science and Technology, 6(2), 53-55. https://doi.org/10.11648/j.ijdst.20200602.11

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ACS Style

Ojo Timothy Ayodele; Alabi Olatayo Olusegun; Oyegoke Adiat Odunayo; Ayinde Liasu Adekunle; Ogunwole Bolawa Adijat. Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. Int. J. Data Sci. Technol. 2020, 6(2), 53-55. doi: 10.11648/j.ijdst.20200602.11

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AMA Style

Ojo Timothy Ayodele, Alabi Olatayo Olusegun, Oyegoke Adiat Odunayo, Ayinde Liasu Adekunle, Ogunwole Bolawa Adijat. Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital. Int J Data Sci Technol. 2020;6(2):53-55. doi: 10.11648/j.ijdst.20200602.11

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@article{10.11648/j.ijdst.20200602.11,
  author = {Ojo Timothy Ayodele and Alabi Olatayo Olusegun and Oyegoke Adiat Odunayo and Ayinde Liasu Adekunle and Ogunwole Bolawa Adijat},
  title = {Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital},
  journal = {International Journal on Data Science and Technology},
  volume = {6},
  number = {2},
  pages = {53-55},
  doi = {10.11648/j.ijdst.20200602.11},
  url = {https://doi.org/10.11648/j.ijdst.20200602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20200602.11},
  abstract = {In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x1), Urea (x2), temperature (x3), Fasting blood sugar (x4), Body mass index (x5), and marital status (x6). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x1+0.008 x2-0.897 x3-0.021 x4-0.017 x5-2.872 x6. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.},
 year = {2020}
}

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TY  - JOUR
T1  - Classification Analysis of Gender Among Diabetic Patients in Nigeria Hospital
AU  - Ojo Timothy Ayodele
AU  - Alabi Olatayo Olusegun
AU  - Oyegoke Adiat Odunayo
AU  - Ayinde Liasu Adekunle
AU  - Ogunwole Bolawa Adijat
Y1  - 2020/08/10
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdst.20200602.11
DO  - 10.11648/j.ijdst.20200602.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 53
EP  - 55
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20200602.11
AB  - In medical research there is few record on scientific method of discriminating and classifying gender statistically into groups of study. The purpose of this study is to use discriminant analysis and classification analysis to classify diabetic patient into groups of gender; to estimate the proportion of observations in each of the prior group; and to estimate the probability of correct classification and misclassification respectively. To this effect, a sample of 152 cases (diabetic patients) was observed with the following measurements: Age (x1), Urea (x2), temperature (x3), Fasting blood sugar (x4), Body mass index (x5), and marital status (x6). The gender was classified into male and female. We observed that the Discriminant Function Z=0.036x1+0.008 x2-0.897 x3-0.021 x4-0.017 x5-2.872 x6. Also 64.5% of the original grouped cases were correctly classified. The percentage of misclassification is 34.5%. Conclusively the measure of the predictive ability which is the percentage of correct classification shows that discriminant analysis can be used to predict diabetic patients into two classes of gender and can also be used to predict group membership of any subject matter.
VL  - 6
IS  - 2
ER  -

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