The covariance matrix supports a variety of multivariate statistical analyses in both theoretical and application areas of studies. Non-positive definite matrices often occur with various causes such as the high dimensionality problem. In this study, three main methods of covariance transformation assessed included; the vine, iterative transform and the shrinkage method. To improve the performance of the shrinkage method, we proposed two novel shrinkage targets: Target W (the Eigenvalue of the true covariance matrix) and Target F (the Eigenvalue of the common target D). Data on the number of patients recorded for 19 diseases in 11 governorates from the Oman Ministry of Health were used to validate the proposed shrinkage targets. Preliminary multivariate statistical tests on the data was done. The two proposed shrinkage targets were assessed against the four existing common targets; A,B,C and D using the Frobenius Norm and Shrinkage Intensity. In all validation tests, the proposed shrinkage targets performed better than the existing common methods, showing improvements of the shrinkage method to transform the non-positive definite covariance as applied to the high dimensional disease data.