Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm

Ebru Pekel Özmen; Tuncay Özcan

doi:10.1007/978-3-030-85626-7_69

Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm

Ebru Pekel Özmen^*, Tuncay Özcan

^*Corresponding author for this work

Department of Management Engineering

Samsun University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Cancer is a group of diseases which are formed by the uncontrolled proliferation and growth of tissues and cells in organs and their treatment and approach are different from each other. Cancer disease shows too fast metastasis. Therefore, early diagnosis and treatment is very important. Technological advances in computer and electronics, early stages of cancer increased the probability of correct diagnosis. Especially in recent years, better results are obtained in the diagnosis of cancer with the studies based on machine learning. In this study, hybrid approaches were proposed for diagnosis of breast cancer. XGBOOST and Artificial Neural Network (ANN) algorithms were employed by hybridizing with genetic algorithm (GA) to improve classification accuracy. The performance analysis of the proposed approaches was presented with ‘Wisconsin’ dataset taken from UCI machine learning repository. Numerical results showed that the proposed hybrid XGBOOST-GA approach significantly outperformed the classical prediction algorithms and the best classification accuracy was achieved.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	589-595
Number of pages	7
ISBN (Print)	9783030856250
DOIs	https://doi.org/10.1007/978-3-030-85626-7_69
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Duration: 24 Aug 2021 → 26 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	307
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/Territory	Turkey
City	Istanbul
Period	24/08/21 → 26/08/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

ANN
Breast cancer
Classification
Genetic algorithm
XGBOOST

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-85626-7_69

Cite this

Pekel Özmen, E., & Özcan, T. (2022). Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 589-595). (Lecture Notes in Networks and Systems; Vol. 307). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85626-7_69

Pekel Özmen, Ebru ; Özcan, Tuncay. / Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 589-595 (Lecture Notes in Networks and Systems).

@inproceedings{940ea63db5eb43ad86605edada21f44d,

title = "Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm",

abstract = "Cancer is a group of diseases which are formed by the uncontrolled proliferation and growth of tissues and cells in organs and their treatment and approach are different from each other. Cancer disease shows too fast metastasis. Therefore, early diagnosis and treatment is very important. Technological advances in computer and electronics, early stages of cancer increased the probability of correct diagnosis. Especially in recent years, better results are obtained in the diagnosis of cancer with the studies based on machine learning. In this study, hybrid approaches were proposed for diagnosis of breast cancer. XGBOOST and Artificial Neural Network (ANN) algorithms were employed by hybridizing with genetic algorithm (GA) to improve classification accuracy. The performance analysis of the proposed approaches was presented with {\textquoteleft}Wisconsin{\textquoteright} dataset taken from UCI machine learning repository. Numerical results showed that the proposed hybrid XGBOOST-GA approach significantly outperformed the classical prediction algorithms and the best classification accuracy was achieved.",

keywords = "ANN, Breast cancer, Classification, Genetic algorithm, XGBOOST",

author = "{Pekel {\"O}zmen}, Ebru and Tuncay {\"O}zcan",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85626-7_69",

language = "English",

isbn = "9783030856250",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Pekel Özmen, E & Özcan, T 2022, Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, vol. 307, Springer Science and Business Media Deutschland GmbH, pp. 589-595, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85626-7_69

Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm. / Pekel Özmen, Ebru; Özcan, Tuncay.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 589-595 (Lecture Notes in Networks and Systems; Vol. 307).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm

AU - Pekel Özmen, Ebru

AU - Özcan, Tuncay

PY - 2022

Y1 - 2022

N2 - Cancer is a group of diseases which are formed by the uncontrolled proliferation and growth of tissues and cells in organs and their treatment and approach are different from each other. Cancer disease shows too fast metastasis. Therefore, early diagnosis and treatment is very important. Technological advances in computer and electronics, early stages of cancer increased the probability of correct diagnosis. Especially in recent years, better results are obtained in the diagnosis of cancer with the studies based on machine learning. In this study, hybrid approaches were proposed for diagnosis of breast cancer. XGBOOST and Artificial Neural Network (ANN) algorithms were employed by hybridizing with genetic algorithm (GA) to improve classification accuracy. The performance analysis of the proposed approaches was presented with ‘Wisconsin’ dataset taken from UCI machine learning repository. Numerical results showed that the proposed hybrid XGBOOST-GA approach significantly outperformed the classical prediction algorithms and the best classification accuracy was achieved.

AB - Cancer is a group of diseases which are formed by the uncontrolled proliferation and growth of tissues and cells in organs and their treatment and approach are different from each other. Cancer disease shows too fast metastasis. Therefore, early diagnosis and treatment is very important. Technological advances in computer and electronics, early stages of cancer increased the probability of correct diagnosis. Especially in recent years, better results are obtained in the diagnosis of cancer with the studies based on machine learning. In this study, hybrid approaches were proposed for diagnosis of breast cancer. XGBOOST and Artificial Neural Network (ANN) algorithms were employed by hybridizing with genetic algorithm (GA) to improve classification accuracy. The performance analysis of the proposed approaches was presented with ‘Wisconsin’ dataset taken from UCI machine learning repository. Numerical results showed that the proposed hybrid XGBOOST-GA approach significantly outperformed the classical prediction algorithms and the best classification accuracy was achieved.

KW - ANN

KW - Breast cancer

KW - Classification

KW - Genetic algorithm

KW - XGBOOST

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M3 - Conference contribution

AN - SCOPUS:85115052476

SN - 9783030856250

T3 - Lecture Notes in Networks and Systems

SP - 589

EP - 595

BT - Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

A2 - Sari, Irem Ucal

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2021

Y2 - 24 August 2021 through 26 August 2021

ER -

Pekel Özmen E, Özcan T. Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editors, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 589-595. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85626-7_69

Diagnosis of Breast Cancer Using Novel Hybrid Approaches with Genetic Algorithm

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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