research-article

Online sequential extreme learning machine approach for breast cancer diagnosis

Authors:

Musatafa Abbas Abbood Albadr,

Fahad Taha AL-Dhief,

Ali Hashim Abbas,

Raad Z. HomodAuthors Info & Claims

Neural Computing and Applications, Volume 36, Issue 18

Pages 10413 - 10429

https://doi.org/10.1007/s00521-024-09617-x

Published: 07 March 2024 Publication History

Abstract

The utilisation of DM (Data Mining) and ML (Machine Learning) approaches in the BC (Breast Cancer) diagnosis has recently gained a lot of consideration. However, most of these works still need enhancement since either they were assessed utilising insufficient evaluation-metrics, or they weren’t statistically-assessed, or both. Lately, one-of-the-most effective and well-known ML approaches is OSELM (Online Sequential Extreme Learning Machine), it has seen as an efficient and reputable technique for classifying-data, however it has not been implemented in BC diagnosis problem. Consequently, this research proposes the OSELM approach in-order-to enhance the rate of accuracy for the BC diagnosis. The OSELM technique has the ability to (a) capability to be applied on both (multi-class and binary) classification, (b) prevent overfitting, as well as (c) It has a comparable ability to kernel-based SVM (Support Vector Machine) and operates with a neural-network-structure. In this research, two different BC datasets (WDBC (Wisconsin Diagnostic Breast Cancer) and WBCD (Wisconsin Breast Cancer Database)) were utilised to evaluate the OSELM approach performance. The experiments outcomes have revealed the outstanding-performance of the proposed OSELM approach, which attained an average of precision 94.09%, recall 95.57%, accuracy 96.13%, G-Mean 94.82%, F-Measure 94.80%, specificity 96.51%, and MCC 91.76% using WDBC dataset. Besides, attained an average of precision 95.08%, recall 98.89%, accuracy 97.89%, G-Mean 96.96%, F-Measure 96.93%, specificity 97.41%, and MCC 95.39% using WBCD dataset. This indicates that the OSELM approach is a reliable technique for the BC diagnosis and might be suitable for solving other-applications-related issues in the sector of healthcare. Besides, it can serve as a valuable decision-support tool for oncologists, providing additional information and insights to aid in their diagnoses and treatment plans.

References

[1]

Khan F et al. Cloud-based breast cancer prediction empowered with soft computing approaches J Healthcare Eng 2020

[2]

Albadr MA et al. Breast cancer diagnosis using fast learning network algorithm Front Oncol 2023 13 1679

[3]

DeSantis CE et al. Breast cancer statistics CA: Cancer J Clin 2019 69 6 438-451

[4]

Briguglio G et al. Polyphenols in cancer prevention: New insights Int J Funct Nutr 2020 1 2 1-1

[5]

Dubey AK, Gupta U, Jain S (2015) A survey on breast cancer scenario and prediction strategy. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014. Springer

[6]

Uzun Ozsahin D et al. The systematic review of artificial intelligence applications in breast cancer diagnosis Diagnostics 2022 13 1 45

[7]

Zhang T et al. Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network PLoS ONE 2022 17 8 e0273355

[8]

Zafiropoulos E, Maglogiannis I, Anagnostopoulos I (2006) A support vector machine approach to breast cancer diagnosis and prognosis. In: IFIP international conference on artificial intelligence applications and innovations. Springer

[9]

Albadr MAA et al. (2021) Extreme learning machine for automatic language identification utilising emotion speech data. In: 2021 international conference on electrical, communication, and computer engineering (ICECCE). IEEE

[10]

Albadr MAA and Tiun S Spoken language identification based on particle swarm optimisation–extreme learning machine approach Circuits Systems Signal Process 2020 39 9 4596-4622

Digital Library

[11]

Albadr MAA et al. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach PLoS ONE 2018 13 4 e0194770

[12]

Albadr MAA et al. Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection PLoS ONE 2020 15 12 e0242899

[13]

Albadr MAA et al. Particle swarm optimization-based extreme learning machine for COVID-19 detection Cogn Comput 2022

[14]

Albadr MAA et al. Speech emotion recognition using optimised genetic algorithm-extreme learning machine Multimed Tools Appl 2022 81 17 1-27

[15]

Al-Dhief FT et al. A survey of voice pathology surveillance systems based on internet of things and machine learning algorithms IEEE Access 2020 8 64514-64533

[16]

AL-Dhief FT et al. (2020) Voice pathology detection using machine learning technique. In: 2020 IEEE 5th international symposium on telecommunication technologies (ISTT). IEEE

[17]

AL-Dhief FT et al. (2021) Voice pathology detection using support vector machine based on different number of voice signals. In: 2021 26th IEEE Asia-Pacific conference on communications (APCC). IEEE.

[18]

Albadr MAA et al. Grey wolf optimization-extreme learning machine approach for diabetic retinopathy detection Front Public Health 2022 10 925901-925901

[19]

Abreu PH et al. Predicting breast cancer recurrence using machine learning techniques: a systematic review ACM Computing Surveys (CSUR) 2016 49 3 1-40

Digital Library

[20]

Fatima N et al. Prediction of breast cancer, comparative review of machine learning techniques, and their analysis IEEE Access 2020 8 150360-150376

[21]

Thomas T, Pradhan N, Dhaka VS (2020) Comparative analysis to predict breast cancer using machine learning algorithms: a survey. In: 2020 international conference on inventive computation technologies (ICICT). IEEE.

[22]

Gardezi SJS et al. Breast cancer detection and diagnosis using mammographic data: systematic review J Med Internet Res 2019 21 7 e14464

[23]

Yue W et al. Machine learning with applications in breast cancer diagnosis and prognosis Designs 2018 2 2 13

[24]

Albadra MAA and Tiuna S Extreme learning machine: a review Int J Appl Eng Res 2017 12 14 4610-4623

[25]

Huang G et al. Trends in extreme learning machines: a review Neural Netw 2015 61 32-48

Digital Library

[26]

Albadr MAA et al. Mel-frequency cepstral coefficient features based on standard deviation and principal component analysis for language identification systems Cogn Comput 2021 13 5 1136-1153

[27]

Utomo CP, Kardiana A, and Yuliwulandari R Breast cancer diagnosis using artificial neural networks with extreme learning techniques Int J Adv Res Artif Intel 2014 3 7 10-14

[28]

Dubey AK, Gupta U, and Jain S Analysis of k-means clustering approach on the breast cancer Wisconsin dataset Int J Comput Assist Radiol Surg 2016 11 11 2033-2047

[29]

Ara S, Das A, Dey A (2021) Malignant and benign breast cancer classification using machine learning algorithms. In: 2021 international conference on artificial intelligence (ICAI). IEEE.

[30]

Kumar P and Nair GG An efficient classification framework for breast cancer using hyper parameter tuned random decision forest classifier and bayesian optimization Biomed Signal Process Control 2021 68 102682

[31]

Naji MA et al. Machine learning algorithms for breast cancer prediction and diagnosis Procedia Computer Science 2021 191 487-492

Digital Library

[32]

Phan AV, Nguyen ML, and Bui LT Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems Appl Intell 2017 46 2 455-469

Digital Library

[33]

Agarap AFM (2018) On breast cancer detection: an application of machine learning algorithms on the Wisconsin diagnostic dataset. In: Proceedings of the 2nd international conference on machine learning and soft computing

[34]

Asri H et al. Using machine learning algorithms for breast cancer risk prediction and diagnosis Procedia Comput Sci 2016 83 1064-1069

[35]

Kumari M and Singh V Breast cancer prediction system Procedia Comput Sci 2018 132 371-376

Digital Library

[36]

Birchha V and Nigam B Performance analysis of averaged perceptron machine learning classifier for breast cancer detection Procedia Comput Sci 2023 218 2181-2190

Digital Library

[37]

Dhahri H et al. Automated breast cancer diagnosis based on machine learning algorithms J Healthcare Eng 2019

[38]

Uddin KMM et al. Machine learning-based diagnosis of breast cancer utilising feature optimization technique ComputMethods Progr Biomed Update 2023 3 100098

[39]

Jabbar MA Breast cancer data classification using ensemble machine learning Eng Appl Sci Res 2021 48 1 65-72

[40]

Kaur H Dense convolutional neural network based deep learning framework for the diagnosis of breast cancer Wirel Pers Commun 2023 132 3 1-16

[41]

Jakkaladiki SP and Maly F An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer PeerJ Comput Sci 2023 9 e1281

[42]

Mekha P and Teeyasuksaet N (2019) Deep learning algorithms for predicting breast cancer based on tumour cells. In: 2019 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT-NCON). IEEE

[43]

William H, Wolberg GSD (1995) Wisconsin Diagnosis Breast Cancer (WDBC). UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)

[44]

Dora L et al. Optimal breast cancer classification using Gauss-Newton representation based algorithm Expert Syst Appl 2017 85 134-145

Digital Library

[45]

Wolberg WH (1992) Breast Cancer Wisconsin (Original) Data Set. UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29

[46]

Huang G-B et al. On-line sequential extreme learning machine Comput Intell 2005 2005 232-237

[47]

Al-Dhief FT et al. Voice pathology detection and classification by adopting online sequential extreme learning machine IEEE Access 2021 9 77293-77306

[48]

Shaila S et al. Analysis and prediction of breast cancer using multi-model classification approach Data engineering and intelligent computing 2022 New York Springer 109-118

[49]

Zeid MAE, El-Bahnasy K, and Abu-Youssef SE An efficient optimised framework for analysing the performance of breast cancer using machine learning algorithms J Theor ApplInf Technol 2022 100 14 5165-78

[50]

Albadr MAA et al. Grey wolf optimization-extreme learning machine for automatic spoken language identification Multimed Tools Appl 2023 82 1-27

[51]

Al-Dhief FT et al. (2022) Dysphonia detection based on voice signals using naive bayes classifier. In: 2022 IEEE 6th international symposium on telecommunication technologies (ISTT). IEEE

[52]

Zhang J et al. A data-model interactive remaining useful life prediction approach of lithium-ion batteries based on PF-BiGRU-TSAM IEEE Trans Ind Inf 2023

[53]

Zhang J et al. Lifetime extension approach based on Levenberg–Marquardt neural network and power routing of DC-DC converters IEEE Trans Power Electron 2023

[54]

Zhang J et al. An integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning under imbalanced sample condition IEEE Trans Neural Netw Learn Syst 2023

[55]

Albadr MA et al. Genetic algorithm based on natural selection theory for optimization problems Symmetry 2020 12 11 1758

[56]

Albadr MAA et al. Spoken language identification based on optimised genetic algorithm–extreme learning machine approach Int J Speech Technol 2019 22 3 711-727

Digital Library

[57]

Zhou Q et al. Decompositions of Taylor diagram and DISO performance criteria Int J Climatol 2021 41 12 5726-5732

[58]

Aalaei S et al. Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets Iran J Basic Med Sci 2016 19 5 476

[59]

Amrane M et al. (2018) Breast cancer classification using machine learning. In: 2018 electric electronics, computer science, biomedical engineerings' meeting (EBBT). IEEE.

[60]

Bashir S, Qamar U, and Khan FH Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble Qual Quant 2015 49 5 2061-2076

[61]

Chaurasia V, Pal S, and Tiwari B Prediction of benign and malignant breast cancer using data mining techniques J Algorithms Comput Technol 2018 12 2 119-126

[62]

Hasan MM, Haque MR, Kabir MM (2019) Breast cancer diagnosis models using PCA and different neural network architectures. In: 2019 international conference on computer, communication, chemical, materials and electronic engineering (IC4ME2). IEEE

[63]

Houfani D et al. Breast cancer classification using machine learning techniques: a comparative study Med Technol J 2020 4 2 535-544

[64]

Huang Z and Chen D A breast cancer diagnosis method based on VIM feature selection and hierarchical clustering random forest algorithm IEEE Access 2021 10 3284-3293

[65]

Idris NF and Ismail MA Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition PeerJ Computer Science 2021 7 e427

[66]

Islam M et al. Breast cancer prediction: a comparative study using machine learning techniques SN Comput Sci 2020 1 5 1-14

Digital Library

[67]

Krishnakumar N and Abdou T (2020) Detection and diagnosis of breast cancer using a Bayesian approach. In: Canadian Conference on Artificial Intelligence. Springer

[68]

Li Y and Chen Z Performance evaluation of machine learning methods for breast cancer prediction Appl Comput Math 2018 7 4 212-216

[69]

Nilashi M et al. A knowledge-based system for breast cancer classification using fuzzy logic method Telematics Inform 2017 34 4 133-144

Digital Library

[70]

Sánchez A et al. Scaled radial axes for interactive visual feature selection: A case study for analysing chronic conditions Expert Syst Appl 2018 100 182-196

Digital Library

[71]

Sarkar SK and Nag A Identifying patients at risk of breast cancer through decision trees Int J Adv Res Comput Sci 2017 8 8 88-91

[72]

Showrov MIH et al. (2019) Performance comparison of three classifiers for the classification of breast cancer dataset. In: 2019 4th international conference on electrical information and communication technology (EICT). IEEE

[73]

Sinha NK et al. Developing a web based system for breast cancer prediction using xgboost classifier Int J Eng Res 2020 9 852-856

Cited By

Zakariah MAl-Razgan MAlfakih T(2024)Pathological voice classification using MEEL features and SVM-TabNet modelSpeech Communication10.1016/j.specom.2024.103100162:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103100
Aymaz S(2024)Boosting medical diagnostics with a novel gradient-based sample selection methodComputers in Biology and Medicine10.1016/j.compbiomed.2024.109165182:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.109165

Recommendations

Breast cancer detection in thermal images using extreme learning machine

Breast cancer is one of the major causes of women death worldwide. WHO organization has reported that 1 in every 12 women could be subjected to a breast abnormality during her lifetime. To increase survival rates, it is found that early detection of ...
Analysis of framelets for breast cancer diagnosis

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at ...
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances

Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 36, Issue 18

Jun 2024

628 pages

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 March 2024

Accepted: 19 February 2024

Received: 27 June 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zakariah MAl-Razgan MAlfakih T(2024)Pathological voice classification using MEEL features and SVM-TabNet modelSpeech Communication10.1016/j.specom.2024.103100162:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.specom.2024.103100
Aymaz S(2024)Boosting medical diagnostics with a novel gradient-based sample selection methodComputers in Biology and Medicine10.1016/j.compbiomed.2024.109165182:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.109165

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents