Effect Z-score Normalization on Accuracy of classification of liver disease
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Abstract
Data normalization is one of the pre-processing approaches where the data is either scaled or transformed to make
an equal contribution of each feature. The success of classification algorithms depends upon the quality of the data to obtain a
generalized predictive model of the classification problem. The importance of data normalization for improving data quality.
Therefore, this study aims to investigate the impact of z-score normalization method on accuracy of classification of liver
diaseas. In this paper we apply z-score normalization on three classification algorithm Artificial Neural Network, Support
Vector Machine, and K-nearest neighbour with two liver datasets. It has been observed from the results classification
algorithms effected with z-score normalization.
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