An Investigation of Malaria Predictors Using Logistic Regression Model

Authors(3) :-Abubakar Boyi Dalatu, Mukhtar Garba, Nwoji Jude Oguejiofor

Although malaria is a disease which is considered the most deadly killer especially to children less than 5 years mainly of African countries, there exists no statistical model for the analysis of its predictors for the case of Kebbi State. In this work a logistic regression model using maximum likelihood estimation is proposed. The application of the model using Kebbi State malaria data established that there is significant relationship between malaria status and such predictors as fever, temperature greater than or equal to 37.5 degree, headache, convulsions, cold, cough or sweating, etc. While age, sex, backache and vomiting are not good predictors of malaria. Doctors, medical practitioners and researchers will find this model useful in predicting malaria prevalence.

Authors and Affiliations

Abubakar Boyi Dalatu
Department of Statistics, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
Mukhtar Garba
Department of Statistics, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
Nwoji Jude Oguejiofor
Department of Computer Science, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria

Logit Function, Logistic regression, Maximum Likelihood Estimation

  1. Agresti, A. (1996). An Introduction to Categorical Data Analysis. John Wiley and Sons, Inc. http://lib.stat.cmu.edu/datasets/agresti
  2. De La Cruz N., Benjamin C., Kirk D., Bobbi G., Natasha I., Stephen A., and Robb D. (2006). Who sleeps under bed nets in Ghana; A doer/non-doer analysis of malaria prevention behaviors. Malaria Journal. Vol. 5, issue 1. pp 61-65.
  3. Hosmer D.W and Lemeshow S. (2000). Applied Logistic Regression. 2nd ed. New York, USA: John Wiley and Sons.
  4. Kleinbaum D. G. and Klein M. (1994). 2nd ed. Logistic Regression: A Self-Learning Text. John Wiley and Sons Publishers, New York. pp 15-26.
  5. Lindsay S.W., David G. H., Robert A. H., Shane A. R., and Stephen G. W. (2010). Assessing the future threat from vivax malaria in the United Kingdom using two markedly different modeling approaches. Malaria Journal. Vol. 9, Issue 1. pp 70-79.
  6. Menard, S. (1995). Applied Logistic Regression Analysis. Sage Publications. Series: Quantitative Applications in the Social Sciences, pp. 106.
  7. Riedel N., Penelope V. J. M. M., Laura G., Elizabeth C. K., Victor M. and Rick W. S. (2010). Geographical patterns and predictors of malaria risk in Zambia: Bayesian geo-statistical modeling of the 2006 Zambia national malaria indicator survey. Malaria Journal. Vol. 9. pp 37.

Publication Details

Published in : Volume 1 | Issue 5 | November-December 2015
Date of Publication : 2015-12-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 153-157
Manuscript Number : IJSRST151537
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Abubakar Boyi Dalatu, Mukhtar Garba, Nwoji Jude Oguejiofor, " An Investigation of Malaria Predictors Using Logistic Regression Model, International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 1, Issue 5, pp.153-157, November-December-2015. Available at doi : 10.32628/IJSRST151537
Journal URL : http://ijsrst.com/IJSRST151537

Article Preview

Contact Us