Data mining plays an important role in medical data analysis. It considers large datasets and is capable of discovering interesting patterns that help to represent and use the nature of medical data. Moreover, these data patterns are useful for the medical practitioners for making effective decisions since it collects the necessary temporal information from large data repositories by applying intelligent data mining techniques. In this paper,a statistical technique is proposed for performing effective decision making in medical application, screening and manipulating the training samples with little bit of Gaussian distribution random values (GDRV) before using the data for training the neural network. This paper present, a way to improve the performance of a neural network based classification model through the proposed algorithm which has been evaluated with the coronary artery disease (CAD) data sets taken from University California Irvine (UCI). The scope of this paper is to present an iterative LDA based classification method for the classification of multivariate data sets. The performance of the proposed iterative LDA based classifier will be evaluated with standard metrics. © Springer India 2015.