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Feature selection and classification using support vector machine and decision tree
Author(s) -
Durgalakshmi B.,
Vijayakumar V.
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12280
Subject(s) - feature selection , breast cancer , big data , computer science , decision tree , support vector machine , selection (genetic algorithm) , data mining , artificial intelligence , cancer , tree (set theory) , feature (linguistics) , machine learning , healthcare system , pattern recognition (psychology) , health care , medicine , mathematics , mathematical analysis , linguistics , philosophy , economics , economic growth
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.