Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis

Ibrahim, Sara; Nazir, Saima; Velastin Carroza, Sergio Alejandro

Publication:
Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)	es
dc.contributor.author	Ibrahim, Sara
dc.contributor.author	Nazir, Saima
dc.contributor.author	Velastin Carroza, Sergio Alejandro
dc.date.accessioned	2021-11-03T08:31:44Z
dc.date.available	2021-11-03T08:31:44Z
dc.date.issued	2021-11
dc.description.abstract	Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.	en
dc.format.extent	16
dc.identifier.bibliographicCitation	Ibrahim, S., Nazir, S. & Velastin, S. A. (2021). Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis. Journal of Imaging, 7(11), 225.	en
dc.identifier.doi	https://doi.org/10.3390/jimaging7110225
dc.identifier.issn	2313-433X
dc.identifier.publicationfirstpage	225
dc.identifier.publicationissue	11
dc.identifier.publicationtitle	Journal of Imaging	en
dc.identifier.publicationvolume	7
dc.identifier.uri	http://hdl.handle.net/10016/33516
dc.identifier.uxxi	AR/0000028513
dc.language.iso	eng
dc.publisher	MDPI
dc.rights	© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Informática	es
dc.subject.other	Breast cancer diagnosis	en
dc.subject.other	Wisconsin breast cancer dataset	en
dc.subject.other	Feature selection	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Ensemble method	en
dc.title	Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication