Discriminant analysis is a technique used to distinguish and classification an individual to a group among a number of groups based on a linear combination of a set of relevant variables know discriminant function. In this research discriminant analysis used to analysis data from repeated measurements design. We will deal with the problem of discrimination and classification in the case of two groups by assuming the Compound Symmetry covariance structure under the assumption of normality for univariate repeated measures data.
The importance of this research represented to find the best model to classify a group of patients who suffer from diabetes. For the purpose of studying the effects of the number of correlations, variances, and umber of repeated measurements on the performance of classification rules for this type of data based on monthly measurements of glycosylated hemoglobin (HbA1C) in the blood was taken in three stages, which is the beginning of the experiment, and after three months, and then six months for two groups of patients, the first group consists of (38) patients was suffered from diabetes type (I) and the second group includes (33) patients suffered from diabetes type (II).
And through this research, concluded that when the number of parameters began to increase. Thus, the apparent error rate begin to increasing, and this is what reduces the efficiency of classification rules for this type of data. And we recommend by using the linear discriminant function when you focus on the least number of parameters to build the classification rule. And quadratic discriminant procedure Represented by equal the variance and different correlation parameters under compound symmetry covariance structures
This work is licensed under a 
