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2021 | OriginalPaper | Buchkapitel

Feature Selection Optimization for Breast Cancer Diagnosis

verfasst von : Ana Rita Antunes, Marina A. Matos, Lino A. Costa, Ana Maria A. C. Rocha, Ana Cristina Braga

Erschienen in: Optimization, Learning Algorithms and Applications

Verlag: Springer International Publishing

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Abstract

Cancer is one of the leading causes of death in the world, which has increased over the past few years. This disease can be classified as benign or malignant. One of the first and most common cancers that appear in the human body is breast cancer, which, as the name implies, appears in the breast regardless of the person’s gender. Machine learning has been widely used to assist in the diagnosis of breast cancer.

In this work, feature selection and multi-objective optimization are applied to the Breast Cancer Wisconsin Diagnostic dataset. It is intended to identify the most relevant characteristics to classify whether the diagnosis is benign or malignant. Two classifiers will be used in the feature selection task, one based on neural networks and the other on support vector machine. The objective functions to be used in the optimization process are to maximize sensitivity and specificity, simultaneously. A comparison was made between the techniques used and there was a better performance by neural networks.

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Vorheriges Kapitel Analysis of the Middle and Long Latency ERP Components in Schizophrenia

Nächstes Kapitel Cluster Analysis for Breast Cancer Patterns Identification

Available in: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic), Accessed: 2021-05-07.

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Titel: Feature Selection Optimization for Breast Cancer Diagnosis
verfasst von: Ana Rita Antunes
Marina A. Matos
Lino A. Costa
Ana Maria A. C. Rocha
Ana Cristina Braga
Verlag: Springer International Publishing
Buch: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_36

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