Data classification using PCA based on Effective Variance Coverage (EVC)

Classification analysis of Medical diseases diagnosis has been performed extensively to find out the biological features and to differentiate intimately related diseases types that usually appear in the diagnosis of diseases. Many algorithms and techniques have been developed for the medical diseases classification process. These developed techniques accomplish feature based classification process with the aid of two basic phases namely dimensionality reduction through feature extraction, dimensionality reduction through feature selection. Among various dimensionality reduction techniques, this paper proposed prescribed statistical procedures to efficiently perform the classification process through feature extraction especially using PCA. To further substantiate and to analyze the performance, we conduct a deep study in Principle Component Analysis (PCA). The dimensionality reduction techniques perform the reduction in features through feature extraction and perform data classification with high accuracy. Based on the obtained results, we conclude that the performance study of PCA based on its Variance Coverage Range (VCR) over the several medical data sets work well. The study results that the statistical approach with PCA outperforms the classification performance.