Group covariates assessment on real-life Diabetes patients by Fractional Polynomials: a study based on Logistic Regression Modeling

The advanced approach to modeling the logistic regression with fractional polynomials is applied in place of the traditional linear predictors to group the continuous covariates for the healthcare dataset. The real-life data obtained from the 500 of diabetic patients in northwestern Nigeria. The statistical modeling and predictions of finding the group covariates analytically based on the patients' variables, age and the occupation, by the theories of "Royston and Altman" and "Royston and Sauerbrei". The algorithm in terms of the selection for key factors with the properties congregates at φ (3, 3) with the deviance ratio of 113.00 and the log likelihood assessment of -56.50 for the model results of patients' age. For the patients' occupation, the algorithm for the key factors with extensive outcomes converged at φ (-2, 3) with the deviance ratio of 111.36 and the log likelihood assessment of -56.43. The analysis modeling approach for the second standard method with the fractional polynomials provides the excellent results on the healthcare dataset to investigate the diabetic status. The method is also sufficient for the metadata of different disease because it produces the minimum deviance and maximum log likelihood values.