Manuscript Number : IJSRST151537

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

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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.
Journal URL : http://ijsrst.com/IJSRST151537

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