Early predictive model for breast cancer classification using blended ensemble learning

Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models rea...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of system assurance engineering and management Vol. 15; no. 1; pp. 188 - 197
Main Authors:	Mahesh, T. R., Vinoth Kumar, V., Vivek, V., Karthick Raghunath, K. M., Sindhu Madhuri, G.
Format:	Journal Article
Language:	English
Published:	New Delhi Springer India 01.01.2024 Springer Nature B.V
Subjects:	Accuracy Breast cancer Cancer therapies Classification Data mining Datasets Decision trees Disease Engineering Engineering Economics Ensemble learning Fuzzy sets Genetic algorithms Logistics Machine learning Mammography Marketing Medical diagnosis Medical prognosis Neural networks Organization Original Article Patients Performance measurement Prediction models Quality Control Reliability Safety and Risk Support vector machines Womens health United States > US Wisconsin Accuracy Precision Machine learning Prediction Breast cancer Recall Diagnosis Detection system
ISSN:	0975-6809, 0976-4348
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models really makes it challenging for clinicians to devise a treatment strategy that will help patients live longer. An automatic illness detection system assists medical personnel in diagnosing disease and provides a reliable, efficient and quick reaction while also lowering the danger of death. A Blended ensemble learning, which is an innovative approach, has been utilized for the classification of breast cancer and this model performs effectively for the base classifier in the prediction analysis. The performance of five machine learning techniques, namely support vector machine, K-nearest neighbors, decision tree Classifier, random forests, and logistic regression, are used as base learners in blended ensemble model. All the incorporated base learners (individually) and the final outcome of the Ensemble Learning are being compared in this study against several performance metrics namely accuracy, recall, precision and f1-score for the early prediction of Breast Cancer. There is a 98.14 percent noticeable improvement with the Ensemble Learning model compared to the basic learners.
AbstractList	Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models really makes it challenging for clinicians to devise a treatment strategy that will help patients live longer. An automatic illness detection system assists medical personnel in diagnosing disease and provides a reliable, efficient and quick reaction while also lowering the danger of death. A Blended ensemble learning, which is an innovative approach, has been utilized for the classification of breast cancer and this model performs effectively for the base classifier in the prediction analysis. The performance of five machine learning techniques, namely support vector machine, K-nearest neighbors, decision tree Classifier, random forests, and logistic regression, are used as base learners in blended ensemble model. All the incorporated base learners (individually) and the final outcome of the Ensemble Learning are being compared in this study against several performance metrics namely accuracy, recall, precision and f1-score for the early prediction of Breast Cancer. There is a 98.14 percent noticeable improvement with the Ensemble Learning model compared to the basic learners.
Author	Karthick Raghunath, K. M. Sindhu Madhuri, G. Vivek, V. Mahesh, T. R. Vinoth Kumar, V.
Author_xml	– sequence: 1 givenname: T. R. orcidid: 0000-0002-5589-8992 surname: Mahesh fullname: Mahesh, T. R. email: trmahesh.1978@gmail.com organization: Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN University – sequence: 2 givenname: V. surname: Vinoth Kumar fullname: Vinoth Kumar, V. organization: Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN University – sequence: 3 givenname: V. surname: Vivek fullname: Vivek, V. organization: Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN University – sequence: 4 givenname: K. M. surname: Karthick Raghunath fullname: Karthick Raghunath, K. M. organization: Department of Computer Science and Engineering, MVJ College of Engineering – sequence: 5 givenname: G. surname: Sindhu Madhuri fullname: Sindhu Madhuri, G. organization: Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN University
BookMark	eNp9UE1LAzEQDVLBWvsHPAU8Rycf-3WUUj-g4EXxGLLZSUnZZmuyFfrv3XYFwUNPM8O8N-_NuyaT0AUk5JbDPQcoHhKXvCoZCMGA51XO4IJMoSpypqQqJ6c-Y3kJ1RWZp7QBAC64Egqm5HNpYnugu4iNt73_RrrtGmyp6yKtI5rUU2uCxUhta1LyzlvT-y7QffJhTesWQ4MNxZBwOwy0RRPDsLkhl860Cee_dUY-npbvixe2ent-XTyumB089wxtU6Grlckzp6TNua0qV6pSGXR5XQi0rsmKgoMta1lnmTPl0bhsGgQQBcgZuRvv7mL3tcfU6023j2GQ1FJkqsqzAT2gyhFlY5dSRKet709_9NH4VnPQxyT1mKQektSnJPVRQPyj7qLfmng4T5IjKQ3gsMb45-oM6weVcIj3
CitedBy_id	crossref_primary_10_3390_e25020245 crossref_primary_10_3390_ani15142033 crossref_primary_10_3390_app131910755 crossref_primary_10_1109_ACCESS_2024_3514662 crossref_primary_10_1109_ACCESS_2023_3253868 crossref_primary_10_3390_app13052885 crossref_primary_10_3390_electronics14091863 crossref_primary_10_3389_fgene_2024_1491602 crossref_primary_10_1155_2022_4451792 crossref_primary_10_1007_s41939_024_00662_6 crossref_primary_10_1007_s11227_025_07331_6 crossref_primary_10_1109_ACCESS_2024_3483095 crossref_primary_10_1186_s12880_024_01267_8 crossref_primary_10_1007_s12065_023_00824_4 crossref_primary_10_1088_1402_4896_ada18d crossref_primary_10_1002_cjce_25594 crossref_primary_10_1109_ACCESS_2024_3480310 crossref_primary_10_1016_j_chemolab_2025_105399 crossref_primary_10_1109_ACCESS_2024_3523745 crossref_primary_10_1016_j_eij_2025_100785 crossref_primary_10_32628_IJSRST24113102 crossref_primary_10_1016_j_patcog_2025_111838 crossref_primary_10_3390_diagnostics13223452 crossref_primary_10_3390_diagnostics15050549 crossref_primary_10_1038_s41598_025_95311_4 crossref_primary_10_3390_info15100641 crossref_primary_10_1007_s00521_025_11287_2 crossref_primary_10_1016_j_genrep_2025_102325 crossref_primary_10_1016_j_imu_2024_101550 crossref_primary_10_1109_ACCESS_2024_3398198
Cites_doi	10.1109/FABS52071.2021.9702566 10.1016/j.artmed.2004.07.002 10.4081/jphr.2019.1677 10.1109/FABS52071.2021.9702699 10.1148/radiol.2017171920 10.1109/RTEICT52294.2021.9573604 10.1007/s12539-021-00418-7 10.18201/ijisae.2018648455 10.1111/j.1468-0394.2008.00480.x 10.1109/C2I451079.2020.9368911 10.1109/TIPTEKNO.2019.8895222 10.3390/jimaging6060039 10.1016/j.patrec.2019.03.022 10.4018/978-1-7998-6870-5.ch025 10.1145/3184066.3184080 10.1109/ICCCNT.2018.8493927 10.1016/j.procs.2016.04.224 10.1109/PIMRC.2017.8292668 10.1109/TKDE.2019.2891622
ContentType	Journal Article
Copyright	The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022 The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022.
Copyright_xml	– notice: The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022 – notice: The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022.
DBID	AAYXX CITATION
DOI	10.1007/s13198-022-01696-0
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	0976-4348
EndPage	197
ExternalDocumentID	10_1007_s13198_022_01696_0
GeographicLocations	United States--US Wisconsin
GeographicLocations_xml	– name: Wisconsin – name: United States--US
GroupedDBID	-EM 06D 0R~ 203 29~ 2JY 30V 406 40D 96X AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH AAZMS ABAKF ABBXA ABDZT ABECU ABFTV ABHQN ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACGFS ACHSB ACIWK ACKNC ACMLO ACOKC ACPIV ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEMSY AEOHA AEPYU AESKC AETCA AEVLU AEXYK AFBBN AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJZVZ AKLTO ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR ANMIH AUKKA AXYYD AYJHY CSCUP DNIVK DPUIP EBLON EIOEI ESBYG FERAY FIGPU FINBP FNLPD FRRFC FSGXE FYJPI GGCAI GGRSB GJIRD GQ6 GQ7 GQ8 HG6 HMJXF HQYDN HRMNR I0C IKXTQ IWAJR IXD IZIGR J-C J0Z JBSCW JCJTX JZLTJ KOV LLZTM NPVJJ NQJWS O93 O9J PT4 R89 RLLFE ROL RSV SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG UG4 UOJIU UTJUX UZXMN VFIZW W48 Z5O Z7R Z7X Z7Z Z88 ZMTXR ~A9 AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION
ID	FETCH-LOGICAL-c319t-ecd9efb4a65f43c61c99f8484aef6b72ecfd57710c8b3b55fa814243dde002703
IEDL.DBID	RSV
ISICitedReferencesCount	42
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000815777400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0975-6809
IngestDate	Sun Sep 28 02:42:03 EDT 2025 Tue Nov 18 21:50:19 EST 2025 Sat Nov 29 04:20:37 EST 2025 Fri Feb 21 02:40:31 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Accuracy Precision Machine learning Prediction Breast cancer Recall Diagnosis Detection system
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-ecd9efb4a65f43c61c99f8484aef6b72ecfd57710c8b3b55fa814243dde002703
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-5589-8992
PQID	3254965243
PQPubID	2043907
PageCount	10
ParticipantIDs	proquest_journals_3254965243 crossref_citationtrail_10_1007_s13198_022_01696_0 crossref_primary_10_1007_s13198_022_01696_0 springer_journals_10_1007_s13198_022_01696_0
PublicationCentury	2000
PublicationDate	20240100 2024-01-00 20240101
PublicationDateYYYYMMDD	2024-01-01
PublicationDate_xml	– month: 1 year: 2024 text: 20240100
PublicationDecade	2020
PublicationPlace	New Delhi
PublicationPlace_xml	– name: New Delhi – name: Dordrecht
PublicationTitle	International journal of system assurance engineering and management
PublicationTitleAbbrev	Int J Syst Assur Eng Manag
PublicationYear	2024
Publisher	Springer India Springer Nature B.V
Publisher_xml	– name: Springer India – name: Springer Nature B.V
References	DelenDAnalysis of cancer data: a data mining approachExpert Syst200926110011210.1111/j.1468-0394.2008.00480.x KharyaSSoniSWeighted naive bayes classifier: a predictive model for breast cancer detectionInt J Comput Appl201613393237 Khan S, Islam N, Jan Z, Din IU, Rodrigues JJ (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1–6 AslanMFCelikYSabanciKDurduABreast cancer diagnosis by different machine learning methods using blood analysis dataInt J Intell Syst Appl Eng20186428929310.18201/ijisae.2018648455 Telsang VA, nd Hegde K (2020) Breast cancer prediction analysis using machine learning algorithms. In: 2020 International conference on communication, computing and industry 4.0 (C2I4) Sarveshvar MR, Gogoi A, Chaubey AK, Rohit S, Mahesh TR (2021a) Performance of different machine learning techniques for the prediction of heart diseases. In: 2021a International conference on forensics, analytics, big data, security (FABS), 2021, pp 1–4 DelenDWalkerGKadamAPredicting breast cancer survivability: a comparison of three data mining methodsArtif Intell Med200534211312710.1016/j.artmed.2004.07.002 Shrestha P, Singh A, Garg R, Sarraf I, Mahesh TR, Sindhu Madhuri G (2021c) Early stage detection of scoliosis using machine learning algorithms. In: 2021c International conference on forensics, analytics, big data, security (FABS), pp 1–4 AsriHMousannifHAl MoatassimeHNoelTUsing machine learning algorithms for breast cancer risk prediction and diagnosisProcedia Comput Sci2016831064106910.1016/j.procs.2016.04.224 Chauhan P, Swami A (2018) Breast cancer prediction using genetic algorithm based ensemble approach. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), 2018, pp 1–8 ShahbazMFaruqSShaheenMMasoodSACancer diagnosis using data mining technologyLife Sci J201291308313 Shashikala HK, Mahesh TR, Vivek V, Sindhu MG, Saravanan C, Baig TZ (2021b) Early detection of spondylosis using point-based image processing techniques. In: 2021b International conference on recent trends on electronics, information, communication & technology (RTEICT), pp 655–659 Kumar D, Swathi P, Jahangir A, Sah NK, Vinothkumar V (2021) Intelligent speech processing technique for suspicious voice call identification using adaptive machine learning approach. In: Advances in computational intelligence and robotics, pp 372–380 Akbugday B (2019) 2019 Medical Technologies Congress (TIPTEKNO)—Izmir, Turkey (2019.10.3–2019.10.5)] 2019 Medical Technologies Congress (TIPTEKNO)—“Classification of Breast Cancer Data Using Machine Learning Algorithms”, pp 1–4 SalodZSinghYComparison of the performance of machine learning algorithms in breast cancer screening and detection: a protocolJ Public Health Res201983jphr-201910.4081/jphr.2019.1677 Breast Cancer (2018) Statistics, Approved by the Cancer.Net Editorial Board, 04/2017. [Online]. http://www.cancer.net/cancer-types/breast-cancer/statistics. Accessed 26 Aug 2018 GuptaMKChandraPA comprehensive survey of data miningInt J Inf Technol202012115 EltalhiSKutraniHBreast cancer diagnosis and prediction using machine learning and data mining techniques: a reviewIOSR J Dent Med Sci20191848594 GuptaPShaliniLAnalysis of machine learning techniques for breast cancer predictionInt J Eng Comput Sci20187052389123895 KelesMKBreast cancer prediction and detection using data mining classification algorithms: a comparative studyTehn Vjesn Tech Gazette2019261149155 Olson DL, Delen D (2008).“Advanced Data Mining Techniques”, Springer, 2008, ISBN: 978–3–540–76917–0. HuangQChenYLiuLTaoDLiXOn combining bi-clustering mining and AdaBoost for breast tumor classificationIEEE Trans Knowl Data Eng202032472873810.1109/TKDE.2019.2891622 Li L, Wu Y, Ou Y, Li Q, Zhou Y, Chen D (2017) [IEEE 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—Montreal, QC, Canada (2017.10.8–2017.10.13)] 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—“Research on machine learning algorithms and feature extraction for time series”, pp 1–5 DhimanGVinoth KumarVKaurASharmaADON: deep learning and optimization-based framework for detection of novel coronavirus disease using x-ray imagesInterdiscip Sci Comput Life Sci20211326027210.1007/s12539-021-00418-7 Agarap AFM (2018) [ACM Press the 2nd international conference—Phu Quoc Island, Viet Nam (2018.02.02–2018.02.04)] Proceedings of the 2nd international conference on machine learning and soft computing—ICMLSC '18—“On breast cancer detection”, pp 5–9 AssiriASNazirSVelastinSABreast tumor classification using an ensemble machine learning methodJ Imaging2020663910.3390/jimaging6060039 ParkSHHanKMethodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and predictionRadiology201828617192010.1148/radiol.2017171920 D Delen (1696_CR8) 2009; 26 Z Salod (1696_CR22) 2019; 8 S Kharya (1696_CR17) 2016; 133 1696_CR26 MK Gupta (1696_CR13) 2020; 12 1696_CR27 H Asri (1696_CR4) 2016; 83 D Delen (1696_CR9) 2005; 34 G Dhiman (1696_CR10) 2021; 13 1696_CR23 1696_CR25 1696_CR20 Q Huang (1696_CR14) 2020; 32 AS Assiri (1696_CR5) 2020; 6 1696_CR19 1696_CR16 1696_CR18 P Gupta (1696_CR12) 2018; 7 M Shahbaz (1696_CR24) 2012; 9 1696_CR2 SH Park (1696_CR21) 2018; 286 1696_CR1 1696_CR7 1696_CR6 MK Keles (1696_CR15) 2019; 26 S Eltalhi (1696_CR11) 2019; 18 MF Aslan (1696_CR3) 2018; 6
References_xml	– reference: DelenDAnalysis of cancer data: a data mining approachExpert Syst200926110011210.1111/j.1468-0394.2008.00480.x – reference: Kumar D, Swathi P, Jahangir A, Sah NK, Vinothkumar V (2021) Intelligent speech processing technique for suspicious voice call identification using adaptive machine learning approach. In: Advances in computational intelligence and robotics, pp 372–380 – reference: Shashikala HK, Mahesh TR, Vivek V, Sindhu MG, Saravanan C, Baig TZ (2021b) Early detection of spondylosis using point-based image processing techniques. In: 2021b International conference on recent trends on electronics, information, communication & technology (RTEICT), pp 655–659 – reference: Sarveshvar MR, Gogoi A, Chaubey AK, Rohit S, Mahesh TR (2021a) Performance of different machine learning techniques for the prediction of heart diseases. In: 2021a International conference on forensics, analytics, big data, security (FABS), 2021, pp 1–4 – reference: Telsang VA, nd Hegde K (2020) Breast cancer prediction analysis using machine learning algorithms. In: 2020 International conference on communication, computing and industry 4.0 (C2I4) – reference: Li L, Wu Y, Ou Y, Li Q, Zhou Y, Chen D (2017) [IEEE 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—Montreal, QC, Canada (2017.10.8–2017.10.13)] 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—“Research on machine learning algorithms and feature extraction for time series”, pp 1–5 – reference: ShahbazMFaruqSShaheenMMasoodSACancer diagnosis using data mining technologyLife Sci J201291308313 – reference: EltalhiSKutraniHBreast cancer diagnosis and prediction using machine learning and data mining techniques: a reviewIOSR J Dent Med Sci20191848594 – reference: KharyaSSoniSWeighted naive bayes classifier: a predictive model for breast cancer detectionInt J Comput Appl201613393237 – reference: AssiriASNazirSVelastinSABreast tumor classification using an ensemble machine learning methodJ Imaging2020663910.3390/jimaging6060039 – reference: Akbugday B (2019) 2019 Medical Technologies Congress (TIPTEKNO)—Izmir, Turkey (2019.10.3–2019.10.5)] 2019 Medical Technologies Congress (TIPTEKNO)—“Classification of Breast Cancer Data Using Machine Learning Algorithms”, pp 1–4 – reference: Olson DL, Delen D (2008).“Advanced Data Mining Techniques”, Springer, 2008, ISBN: 978–3–540–76917–0. – reference: HuangQChenYLiuLTaoDLiXOn combining bi-clustering mining and AdaBoost for breast tumor classificationIEEE Trans Knowl Data Eng202032472873810.1109/TKDE.2019.2891622 – reference: ParkSHHanKMethodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and predictionRadiology201828617192010.1148/radiol.2017171920 – reference: DelenDWalkerGKadamAPredicting breast cancer survivability: a comparison of three data mining methodsArtif Intell Med200534211312710.1016/j.artmed.2004.07.002 – reference: Chauhan P, Swami A (2018) Breast cancer prediction using genetic algorithm based ensemble approach. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), 2018, pp 1–8 – reference: KelesMKBreast cancer prediction and detection using data mining classification algorithms: a comparative studyTehn Vjesn Tech Gazette2019261149155 – reference: DhimanGVinoth KumarVKaurASharmaADON: deep learning and optimization-based framework for detection of novel coronavirus disease using x-ray imagesInterdiscip Sci Comput Life Sci20211326027210.1007/s12539-021-00418-7 – reference: GuptaPShaliniLAnalysis of machine learning techniques for breast cancer predictionInt J Eng Comput Sci20187052389123895 – reference: AslanMFCelikYSabanciKDurduABreast cancer diagnosis by different machine learning methods using blood analysis dataInt J Intell Syst Appl Eng20186428929310.18201/ijisae.2018648455 – reference: AsriHMousannifHAl MoatassimeHNoelTUsing machine learning algorithms for breast cancer risk prediction and diagnosisProcedia Comput Sci2016831064106910.1016/j.procs.2016.04.224 – reference: Khan S, Islam N, Jan Z, Din IU, Rodrigues JJ (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1–6 – reference: Shrestha P, Singh A, Garg R, Sarraf I, Mahesh TR, Sindhu Madhuri G (2021c) Early stage detection of scoliosis using machine learning algorithms. In: 2021c International conference on forensics, analytics, big data, security (FABS), pp 1–4 – reference: Breast Cancer (2018) Statistics, Approved by the Cancer.Net Editorial Board, 04/2017. [Online]. http://www.cancer.net/cancer-types/breast-cancer/statistics. Accessed 26 Aug 2018 – reference: Agarap AFM (2018) [ACM Press the 2nd international conference—Phu Quoc Island, Viet Nam (2018.02.02–2018.02.04)] Proceedings of the 2nd international conference on machine learning and soft computing—ICMLSC '18—“On breast cancer detection”, pp 5–9 – reference: GuptaMKChandraPA comprehensive survey of data miningInt J Inf Technol202012115 – reference: SalodZSinghYComparison of the performance of machine learning algorithms in breast cancer screening and detection: a protocolJ Public Health Res201983jphr-201910.4081/jphr.2019.1677 – ident: 1696_CR23 doi: 10.1109/FABS52071.2021.9702566 – volume: 34 start-page: 113 issue: 2 year: 2005 ident: 1696_CR9 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2004.07.002 – volume: 8 start-page: jphr-2019 issue: 3 year: 2019 ident: 1696_CR22 publication-title: J Public Health Res doi: 10.4081/jphr.2019.1677 – ident: 1696_CR26 doi: 10.1109/FABS52071.2021.9702699 – volume: 286 start-page: 171920 year: 2018 ident: 1696_CR21 publication-title: Radiology doi: 10.1148/radiol.2017171920 – ident: 1696_CR25 doi: 10.1109/RTEICT52294.2021.9573604 – ident: 1696_CR20 – volume: 13 start-page: 260 year: 2021 ident: 1696_CR10 publication-title: Interdiscip Sci Comput Life Sci doi: 10.1007/s12539-021-00418-7 – volume: 9 start-page: 308 issue: 1 year: 2012 ident: 1696_CR24 publication-title: Life Sci J – volume: 6 start-page: 289 issue: 4 year: 2018 ident: 1696_CR3 publication-title: Int J Intell Syst Appl Eng doi: 10.18201/ijisae.2018648455 – volume: 26 start-page: 100 issue: 1 year: 2009 ident: 1696_CR8 publication-title: Expert Syst doi: 10.1111/j.1468-0394.2008.00480.x – ident: 1696_CR27 doi: 10.1109/C2I451079.2020.9368911 – ident: 1696_CR2 doi: 10.1109/TIPTEKNO.2019.8895222 – volume: 6 start-page: 39 issue: 6 year: 2020 ident: 1696_CR5 publication-title: J Imaging doi: 10.3390/jimaging6060039 – ident: 1696_CR16 doi: 10.1016/j.patrec.2019.03.022 – ident: 1696_CR18 doi: 10.4018/978-1-7998-6870-5.ch025 – ident: 1696_CR1 doi: 10.1145/3184066.3184080 – volume: 18 start-page: 85 issue: 4 year: 2019 ident: 1696_CR11 publication-title: IOSR J Dent Med Sci – ident: 1696_CR7 doi: 10.1109/ICCCNT.2018.8493927 – volume: 26 start-page: 149 issue: 1 year: 2019 ident: 1696_CR15 publication-title: Tehn Vjesn Tech Gazette – ident: 1696_CR6 – volume: 83 start-page: 1064 year: 2016 ident: 1696_CR4 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2016.04.224 – volume: 7 start-page: 23891 issue: 05 year: 2018 ident: 1696_CR12 publication-title: Int J Eng Comput Sci – volume: 133 start-page: 32 issue: 9 year: 2016 ident: 1696_CR17 publication-title: Int J Comput Appl – ident: 1696_CR19 doi: 10.1109/PIMRC.2017.8292668 – volume: 12 start-page: 1 year: 2020 ident: 1696_CR13 publication-title: Int J Inf Technol – volume: 32 start-page: 728 issue: 4 year: 2020 ident: 1696_CR14 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2891622
SSID	ssj0001214240
Score	2.4639223
Snippet	Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	188
SubjectTerms	Accuracy Breast cancer Cancer therapies Classification Data mining Datasets Decision trees Disease Engineering Engineering Economics Ensemble learning Fuzzy sets Genetic algorithms Logistics Machine learning Mammography Marketing Medical diagnosis Medical prognosis Neural networks Organization Original Article Patients Performance measurement Prediction models Quality Control Reliability Safety and Risk Support vector machines Womens health
Title	Early predictive model for breast cancer classification using blended ensemble learning
URI	https://link.springer.com/article/10.1007/s13198-022-01696-0 https://www.proquest.com/docview/3254965243
Volume	15
WOSCitedRecordID	wos000815777400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLink Contemporary customDbUrl: eissn: 0976-4348 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001214240 issn: 0975-6809 databaseCode: RSV dateStart: 20100301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZ4HeDAGzEYKAduUKlb0yQ9IsTEaUI8d6uaNJmQxpi6wu_Hzlo6ECCBeoqapJHtJrZjfwY4ocxkI2QY5B3nAu6EC3Qk48Am-EhpCD_GF5uQ_b4aDJLrKilsWke711eSfqdukt1QWlRA0eeEIIJ28CIs43GnqGDDze3DnGeFsre8cyXBrwoVJlW2zPfTfD6RGjXzy82oP3B6G_9b6iasVwomO59JxBYs2PE2rM3BDu7Ao0c1ZpOCLmlou2O-Hg5D_ZVpClIvmSFhKJgh3ZqCiTz_GAXJD5keebc5QwPYPmODVZUnhrtw37u8u7gKqgILgcEFloE1eWKd5pmIHY-M6JgkcYornlkntOxa4_JYog5ilI50HLtMEWkj3BLJnA2jPVgav4ztPrCISyE4sgMHcZ7pLHQ6y5XNuzLPney2oFMTOTUV-jgVwRilDW4yES1FoqWeaGnYgtOPMZMZ9savvds179LqP5ymEdm_IsY1t-Cs5lXz-ufZDv7W_RBWu6jtzHwzbVgqi1d7BCvmrXyaFsdePt8BhFne7Q
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fS8MwED50CuqDv8Xp1Dz4poWuTZP2UcQxcQ7RqXsrTZoMYc7RVf9-c1lrp6ig9Kk0acPlmnx3ufsO4BgzkyXjrpM2tXaoZtoRPg8cFZmLc4n8MbbYBO92w34_uimSwiZltHt5JGlX6irZzWhL6GD0OTKIGDt4Hhao2bGQMf_27mHGs4LZW9a5EpmvstCNimyZ71_zeUeqYOaXk1G74bTW_jfUdVgtACY5m2rEBsyp0SaszNAObsGjZTUm4wwPaXC5I7YeDjH4lQgMUs-JRGXIiERsjcFEdv4IBskPiBhatzkxBrB6NjekqDwx2Ib71kXvvO0UBRYcaQaYO0qmkdKCJizQ1JesKaNIhzSkidJMcE9JnQbcYBAZCl8EgU5CFK1vlkQ0Z11_B2qjl5HaBeJTzhg16MN0ojQRiatFkoYq9Xiaau7VoVkKOZYF-zgWwRjGFW8yCi02Qout0GK3DicffcZT7o1fWzfKuYuL_3AS-2j_ssCMuQ6n5VxVj39-297fmh_BUrt33Yk7l92rfVj2DPKZ-mkaUMuzV3UAi_Itf5pkh1ZX3wEAV-HR
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED90iuiD3-J0ah5802K3pkn7KOpQlDHwa2-lSZMhzDq66t9vLm3tFBVE-lSahONyTe7zdwCHWJksGXedpK21QzXTjvC476jQPJxLxI-xzSZ4rxcMBmF_qorfZrtXIcmipgFRmtL8ZJzok7rwzUhO4GAmOqKJGJt4FuYoJtKjvX77MOVlwUou62gJDQUscMOycub7ZT7fTrXK-SVKai-f7sr_yV6F5VLxJKeFpKzBjErXYWkKjnADHi3aMRlnGLzBY5DYPjnE6LVEYPJ6TiQKSUYk6tyYZGT3lWDy_JCIkXWnE2MYq2fzQsqOFMNNuO9e3J1dOmXjBUcaAnNHySRUWtCY-Zp6krVlGOqABjRWmgneUVInPje6iQyEJ3xfxwGy2TNHJZq5rrcFjfQlVdtAPMoZo0YrMZMojUXsahEngUo6PEk07zShXTE8kiUqOTbHGEU1njIyLTJMiyzTIrcJRx9zxgUmx6-jW9U-RuX_OYk8tIuZb2huwnG1b_Xnn1fb-dvwA1jon3ejm6ve9S4sdoxCVLhvWtDIs1e1B_PyLX-aZPtWbN8BkALqtQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Early+predictive+model+for+breast+cancer+classification+using+blended+ensemble+learning&rft.jtitle=International+journal+of+system+assurance+engineering+and+management&rft.au=Mahesh%2C+T.+R&rft.au=Vinoth+Kumar%2C+V&rft.au=Vivek%2C+V&rft.au=Karthick+Raghunath%2C+K.+M&rft.date=2024-01-01&rft.pub=Springer+Nature+B.V&rft.issn=0975-6809&rft.eissn=0976-4348&rft.volume=15&rft.issue=1&rft.spage=188&rft.epage=197&rft_id=info:doi/10.1007%2Fs13198-022-01696-0&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0975-6809&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0975-6809&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0975-6809&client=summon