Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis

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 p...

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Autores principales:	Sara Ibrahim, Saima Nazir, Sergio A. Velastin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast cancer diagnosis Wisconsin Breast Cancer Dataset feature selection dimensionality reduction principal component analysis ensemble method Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/5da96af79c0347d49ebcce66b62ce0ff
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spelling	oai:doaj.org-article:5da96af79c0347d49ebcce66b62ce0ff2021-11-25T18:03:25ZFeature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis10.3390/jimaging71102252313-433Xhttps://doaj.org/article/5da96af79c0347d49ebcce66b62ce0ff2021-10-01T00:00:00Zhttps://www.mdpi.com/2313-433X/7/11/225https://doaj.org/toc/2313-433XBreast 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%.Sara IbrahimSaima NazirSergio A. VelastinMDPI AGarticlebreast cancer diagnosisWisconsin Breast Cancer Datasetfeature selectiondimensionality reductionprincipal component analysisensemble methodPhotographyTR1-1050Computer applications to medicine. Medical informaticsR858-859.7Electronic computers. Computer scienceQA75.5-76.95ENJournal of Imaging, Vol 7, Iss 225, p 225 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer diagnosis Wisconsin Breast Cancer Dataset feature selection dimensionality reduction principal component analysis ensemble method Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
spellingShingle	breast cancer diagnosis Wisconsin Breast Cancer Dataset feature selection dimensionality reduction principal component analysis ensemble method Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95 Sara Ibrahim Saima Nazir Sergio A. Velastin Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
description	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%.
format	article
author	Sara Ibrahim Saima Nazir Sergio A. Velastin
author_facet	Sara Ibrahim Saima Nazir Sergio A. Velastin
author_sort	Sara Ibrahim
title	Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
title_short	Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
title_full	Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
title_fullStr	Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
title_full_unstemmed	Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis
title_sort	feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5da96af79c0347d49ebcce66b62ce0ff
work_keys_str_mv	AT saraibrahim featureselectionusingcorrelationanalysisandprincipalcomponentanalysisforaccuratebreastcancerdiagnosis AT saimanazir featureselectionusingcorrelationanalysisandprincipalcomponentanalysisforaccuratebreastcancerdiagnosis AT sergioavelastin featureselectionusingcorrelationanalysisandprincipalcomponentanalysisforaccuratebreastcancerdiagnosis
_version_	1718411681819787264

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis

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