This paper model the effects diabetes risk factors using modified logistic regression model since OLSM is deficient especially when dealing with irreversible variables. A real data was sampled from MOPD unit, Federal Medical Centre Azare using systematic sampling technique. A total sampled of 407 patientsÂ’ data were used to test and compare the modified logistic regression model (MLRM) and the other competitive model. GRETL and SPSS programming bundles were utilized during the information analysis. The result reveals that MLRM is more efficient that LRM. Chi-square was employed to test for dependency between diabetes status (DBStatus) and the other suspected variables; it was shown that there is association between DBStatus and virtually all the variables incorporated in the MLRM model. The MLRM model can be used to predict the chance of an individual to be diabetic given data as input variables. Based on this model, it was observed that the reduced MLRM Model M2 is the suitable model than M1that is the Model with all the variables. This paper also revealed that blood pressure systolic, weight, height blood pressure diastolic, age and sugar level are statistically significant while the other remaining variables are not statistically significant. We recommend for further investigation using machine learning approach.
Key words: Model, Diabetes, Status, Patient , Data, Blood Pressure (BP), MLE, OLS and Probability
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