An Investigation of Malaria Predictors Using Logistic Regression Model
Keywords:
Logit Function, Logistic regression, Maximum Likelihood EstimationAbstract
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.
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