A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are inve...

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Bibliographic Details
Main Authors: Yogendra Singh Solanki, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Vadim Bolshev, Alexander Vinogradov, Elzbieta Jasinska, Radomir Gono, Mohammad Nami
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Electronics
Subjects:
breast cancer prognosis
supervised machine learning classifier
data selection
imbalance handling
Online Access:https://www.mdpi.com/2079-9292/10/6/699

Internet

https://www.mdpi.com/2079-9292/10/6/699

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