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...
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| Vydáno v: | International journal of system assurance engineering and management Ročník 15; číslo 1; s. 188 - 197 |
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| Jazyk: | angličtina |
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New Delhi
Springer India
01.01.2024
Springer Nature B.V |
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| ISSN: | 0975-6809, 0976-4348 |
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| 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. |
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| 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 |
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| 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 |
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| 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. 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| 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 |
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