Logistic regression model deals with the relationship that exists between a dependent variable and one or more independent variables. It provides a method for modeling a binary response variable, which takes values 1 and 0. Further, Logistic regression is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous.Logistic regression model have been applied in a number of contexts. Some examples include applications to adjust for “bias” in comparing two groups in observational studies( Rosenbaun & Rubin,1998). Effron, compared logistic regression to discriminant analysis (which assumes the explanatory variables are multivariate normal at each level of the response variable); it has also been applied to a study investigating the risk factors for low birth weight babies (Hosmer&Lemeshow,1998). Other applications include using logistic regression analysis to determine the factors that affect ”green card” usage for health services(Sansli & Gozde,2006) . Application of logistic regression have also been extended to cases where the dependent variable is more than two cases, known as multinomial or polychotomous (Tabachanick & Fidell 1996).
The aim of the study is to build a model using
the six symptoms variables to predict the presence or absence of Tuberculosis
and malaria in a sample of patients. The model can then be used to derive
estimates of the odds ratios for each factor. Estimate the effects of malaria
and tuberculosis on CD4 count on the recovery rate of HIV/AIDS patients.
Estimate the effect of malaria and tuberculosis on CD4 count on their rate of
response to drugs. Estimate the percentage of HIV/AIDS patients with malaria
and tuberculosis. Since the CD4 cell counts has been an important surrogate
marker for HIV prognosis, attempt were made in this study to examine any
possible impact of opportunistic or concurrent infections with HIV/AIDS on the
CD4 cell counts. This research focused on tuberculosis(TB), as the most
prevalent opportunistic infection and malaria, as the most prevalent endemic
Obioma, O (2018). LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV. Afribary.com: Retrieved February 18, 2019, from https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv
ONUKWUBE, Obioma. "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV" Afribary.com. Afribary.com, 30 Nov. 2018, https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv . Accessed 18 Feb. 2019.
ONUKWUBE, Obioma. "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV". Afribary.com, Afribary.com, 30 Nov. 2018. Web. 18 Feb. 2019. < https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv >.
ONUKWUBE, Obioma. "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV" Afribary.com (2018). Accessed February 18, 2019. https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv