Feature selection and classification using support vector machine and decision tree. (25th February 2020)

• Record Type: Journal Article
• Authors: Durgalakshmi, B.; Vijayakumar, V.
• Is Part Of: Computational intelligence. Volume 36:Number 4(2020)
• Page Start: 1480
• Page End: 1492
• Publication Date: 2020-02-25
• Subjects: big data -- breast cancer -- feature selection -- WDBC; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12280
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Breast cancer is one of the human threats which cause morbidity and mortality worldwide. The death rate can be reduced by advanced diagnosis. The objective of this article is to select the reduced number of features the help in diagnosing breast cancer in Wisconsin Diagnostic Breast Cancer (WDBC). This proposed model depicts women who all have no cancer cells or in benign stage later develop into malignant (metastases). Due to the dynamic nature of the big data framework, the proposed method ensures high confidence and low execution time. Moreover, healthcare information growth chases an exponential pattern, and current database systems cannot adequately manage the massive amount of data. So, it is requisite to adopt the "big data" solution for healthcare information.