1. Exploring Efficient Preprocessing Techniques for Breast Cancer Diagnosis
- Author
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Y. K. Anupama, S. Amutha, and D. R. Ramesh Babu
- Subjects
Covariance matrix ,business.industry ,Computer science ,Pattern recognition ,medicine.disease ,Data preparation ,Original data ,ComputingMethodologies_PATTERNRECOGNITION ,Breast cancer ,Dimension (vector space) ,Principal component analysis ,medicine ,Preprocessor ,Artificial intelligence ,business - Abstract
Preprocessing is a significant data preparation step in data mining or machine learning techniques, to improve the performance of the classification models and to obtain better results. Exploring efficient preprocessing technique plays a vital role. In this study, principle component analysis (PCA) preprocessing technique compared with the proposed Preprocess_Integration(PI) preprocessing technique. The PCA is one of a preprocessing technique that reduces the dimension of the original data. The algorithm proposed in this paper integration of the PCA, correlation matrix, covariance matrix, and Chi-square test. Both the PCA and proposed methodology are applied on the breast cancer dataset. Results obtained with PCA are better than the PI technique.
- Published
- 2021
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