{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:23:42Z","timestamp":1742973822053,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819907403"},{"type":"electronic","value":"9789819907410"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-0741-0_24","type":"book-chapter","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T09:04:34Z","timestamp":1680253474000},"page":"329-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier"],"prefix":"10.1007","author":[{"given":"Mohammad Nasir","family":"Abdullah","sequence":"first","affiliation":[]},{"given":"Bee Wah","family":"Yap","sequence":"additional","affiliation":[]},{"given":"Nik Nur Fatin Fatihah","family":"Sapri","sequence":"additional","affiliation":[]},{"given":"Wan Fairos","family":"Wan Yaacob","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,1]]},"reference":[{"issue":"1","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-05682-1","volume":"12","author":"AL Whittaker","year":"2022","unstructured":"Whittaker, A.L., George, R.P., O\u2019Malley, L.: Prevalence of cognitive impairment following chemotherapy treatment for breast cancer: a systematic review and meta-analysis. Sci. Rep. 12(1), 1\u201322 (2022). https:\/\/doi.org\/10.1038\/s41598-022-05682-1","journal-title":"Sci. Rep."},{"key":"24_CR2","doi-asserted-by":"publisher","unstructured":"Khan, M.H.M., et al.: Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN). PLoS One 16(8), 1\u201315 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0256500","DOI":"10.1371\/journal.pone.0256500"},{"key":"24_CR3","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/978-981-15-7106-0_56","volume-title":"Machine Learning for Predictive Analysis","author":"S Sharma","year":"2021","unstructured":"Sharma, S., Deshpande, S.: Breast cancer classification using machine learning algorithms. In: Joshi, A., Khosravy, M., Gupta, N. (eds.) Machine Learning for Predictive Analysis. LNNS, vol. 141, pp. 571\u2013578. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-7106-0_56"},{"key":"24_CR4","unstructured":"Vohra, P.K., Bhavani, B., Gonthina, N.: Multi-class classification of breast cancer using machine learning. Int. J. Res. Signal Process. Comput. Commun. Syst. Des. 4(2), 33\u201335 (2018)"},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Bihis, M., Roychowdhury, S.: A generalized flow for multi-class and binary classification tasks: an azure ML approach. In: Proceedings of the - 2015 IEEE International Conference Big Data, IEEE Big Data 2015, pp. 1728\u20131737 (2015). https:\/\/doi.org\/10.1109\/BigData.2015.7363944","DOI":"10.1109\/BigData.2015.7363944"},{"issue":"3","key":"24_CR6","first-page":"127","volume":"1","author":"YIA Rejani","year":"2009","unstructured":"Rejani, Y.I.A., Selvi, S.T.: Early detection of breast cancer using SVM classifier technique. Int. J. Comput. Sci. Eng. 1(3), 127\u2013130 (2009)","journal-title":"Int. J. Comput. Sci. Eng."},{"issue":"2","key":"24_CR7","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","volume":"36","author":"MF Akay","year":"2009","unstructured":"Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240\u20133247 (2009). https:\/\/doi.org\/10.1016\/j.eswa.2008.01.009","journal-title":"Expert Syst. Appl."},{"key":"24_CR8","doi-asserted-by":"publisher","unstructured":"Goyal, N., Chandra Trivedi, M.: Breast cancer classification and identification using machine learning approaches. Mater. Today Proc., 1\u20134 (2020). https:\/\/doi.org\/10.1016\/j.matpr.2020.10.666","DOI":"10.1016\/j.matpr.2020.10.666"},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.measurement.2015.04.028","volume":"72","author":"M Karabatak","year":"2015","unstructured":"Karabatak, M.: A new classifier for breast cancer detection based on Na\u00efve Bayesian. Measurement 72, 32\u201336 (2015). https:\/\/doi.org\/10.1016\/j.measurement.2015.04.028","journal-title":"Measurement"},{"key":"24_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106951","volume":"223","author":"H Aljuaid","year":"2022","unstructured":"Aljuaid, H., Alturki, N., Alsubaie, N., Cavallaro, L., Liotta, A.: Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning. Comput. Methods Programs Biomed. 223, 106951 (2022). https:\/\/doi.org\/10.1016\/j.cmpb.2022.106951","journal-title":"Comput. Methods Programs Biomed."},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Nguyen, P.T., Nguyen, T.T., Nguyen, N.C., Le, T.T.: Multiclass breast cancer classification using convolutional neural network. In: Proceedings of the - 2019 International Symposium on Electrical and Electronics Engineering ISEE 2019, pp.130\u2013134 (2019). https:\/\/doi.org\/10.1109\/ISEE2.2019.8920916","DOI":"10.1109\/ISEE2.2019.8920916"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","volume":"83","author":"H Asri","year":"2016","unstructured":"Asri, H., Mousannif, H., Al Moatassime, H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064\u20131069 (2016). https:\/\/doi.org\/10.1016\/j.procs.2016.04.224","journal-title":"Procedia Comput. Sci."},{"issue":"9","key":"24_CR13","doi-asserted-by":"publisher","first-page":"6074","DOI":"10.30534\/ijeter\/2020\/191892020","volume":"8","author":"R Rawal","year":"2020","unstructured":"Rawal, R.: Breast cancer prediction using machine learning. Int. J. Emerg. Trends Eng. Res. 8(9), 6074\u20136079 (2020). https:\/\/doi.org\/10.30534\/ijeter\/2020\/191892020","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"issue":"2","key":"24_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/bdcc6020040","volume":"6","author":"AR Abbas","year":"2022","unstructured":"Abbas, A.R., Mahdi, B.S., Fadhil, O.Y.: Breast and lung anticancer peptides classification using N-Grams and ensemble learning techniques. Big Data Cogn. Comput. 6(2), 40 (2022)","journal-title":"Big Data Cogn. Comput."},{"issue":"4","key":"24_CR15","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1089\/cmb.2018.0238","volume":"26","author":"BI Feltes","year":"2019","unstructured":"Feltes, B.I., Chandelier, B.C., Grisci, E.B., Dorn, M.: CuMiDa: an extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research. J. Comput. Biol. 26(4), 376\u2013386 (2019). https:\/\/doi.org\/10.1089\/cmb.2018.0238","journal-title":"J. Comput. Biol."},{"key":"24_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression, 398","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer, D.W., Jr., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, 398. John Wiley, New York (2013)"},{"key":"24_CR17","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2012","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Third. Morgan Kaufmann, Burlington (2012)"},{"issue":"1","key":"24_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"24_CR19","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-287-936-3","volume-title":"Soft computing in data science","year":"2015","unstructured":"Berry, M.W., Mohamed, A.H., Wah, Y.B. (eds.): SCDS 2015. CCIS, vol. 545. Springer, Singapore (2015). https:\/\/doi.org\/10.1007\/978-981-287-936-3"},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2013.12.060","volume":"266","author":"L Rutkowski","year":"2014","unstructured":"Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. (Ny) 266, 1\u201315 (2014). https:\/\/doi.org\/10.1016\/j.ins.2013.12.060","journal-title":"Inf. Sci. (Ny)"},{"issue":"9","key":"24_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v015.i09","volume":"15","author":"A Karatzoglou","year":"2006","unstructured":"Karatzoglou, A., Meyer, D., Hornik, K.: Support vector machines in R. J. Stat. Softw. 15(9), 1\u201328 (2006)","journal-title":"J. Stat. Softw."},{"key":"24_CR22","unstructured":"Fauvel, M., et al.: Evaluation of kernels for multiclass classification of hyperspectral remote sensing data. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (2006)"},{"key":"24_CR23","unstructured":"R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021)"},{"key":"24_CR24","doi-asserted-by":"publisher","DOI":"10.28919\/cmbn\/4584","author":"RE Caraka","year":"2020","unstructured":"Caraka, R.E., Nugroho, N.T., Tai, S.K., Chen, R.C., Toharudin, T., Pardamean, B.: Feature importance of the aortic anatomy on endovascular aneurysm repair (Evar) using boruta and bayesian mcmc. Commun. Math. Biol. Neurosci. (2020). https:\/\/doi.org\/10.28919\/cmbn\/4584","journal-title":"Commun. Math. Biol. Neurosci."},{"issue":"3","key":"24_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pntd.0004497","volume":"10","author":"M Singla","year":"2016","unstructured":"Singla, M., et al.: Immune response to dengue virus infection in pediatric patients in New Delhi, India\u2014Association of viremia, inflammatory mediators and monocytes with disease severity. PLoS Negl. Trop. Dis. 10(3), 1\u201325 (2016). https:\/\/doi.org\/10.1371\/journal.pntd.0004497","journal-title":"PLoS Negl. Trop. Dis."},{"key":"24_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2022.106485","volume":"217","author":"B Das","year":"2022","unstructured":"Das, B., et al.: Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies. CATENA 217, 106485 (2022). https:\/\/doi.org\/10.1016\/j.catena.2022.106485","journal-title":"CATENA"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Data Science and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-0741-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:51:02Z","timestamp":1685479862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-0741-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819907403","9789819907410"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-0741-0_24","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DaSET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Data Science and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"daset2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdaset.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}