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Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAP-SVM


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Asri, H., Mousannif, H., Moatassime, H., & Noel, T. (2016). Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science, 83, 1064–1069.AsriH.MousannifH.MoatassimeH.NoelT.2016Using machine learning algorithms for breast cancer risk prediction and diagnosisProcedia Computer Science831064106910.1016/j.procs.2016.04.224Search in Google Scholar

Bashir, S., Qamar, U., & Khan, F. (2015). Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble. Qual. Quant., 49(5), 2061–2076.BashirS.QamarU.KhanF.2015Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensembleQual. Quant.4952061207610.1007/s11135-014-0090-zSearch in Google Scholar

Breit, C., Ablah, E., Ward, M., Okut, H., & Tenofsky, P. (2019). Breast cancer risk assessment in patients who test negative for a hereditary cancer syndrome. The American Journal of Surgery, 219(3), 430–433.BreitC.AblahE.WardM.OkutH.TenofskyP.2019Breast cancer risk assessment in patients who test negative for a hereditary cancer syndromeThe American Journal of Surgery219343043310.1016/j.amjsurg.2019.10.01531635794Search in Google Scholar

Chaurasia, V., & Pal, S. (2007). Data mining techniques: To predict and resolve breast cancer survivability. International Journal of Computer Science and Mobile Computing IJCSMC, 3(1), 10–23.ChaurasiaV.PalS.2007Data mining techniques: To predict and resolve breast cancer survivabilityInternational Journal of Computer Science and Mobile Computing IJCSMC311023Search in Google Scholar

Cortes, C., & Vapnik V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.CortesC.VapnikV.1995Support-vector networksMachine Learning20327329710.1007/BF00994018Search in Google Scholar

Elter, M., Schulz-Wendtland, R., & Wittenberg, T. (2007). The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Medical Physics, 34(11), 4164–4172.ElterM.Schulz-WendtlandR.WittenbergT.2007The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision processMedical Physics34114164417210.1118/1.278686418072480Search in Google Scholar

Forsyth, A., Barzilay, R., Hughes, K., Lui, D., Lorenz, K., Enzinger, A., Tulsky, J., & Lindvall, C. (2018). Machine learning methods to extract documentation of breast cancer symptoms from electronic health records. Journal of Pain and Symptom Management, 55(6), 1492–1499.ForsythA.BarzilayR.HughesK.LuiD.LorenzK.EnzingerA.TulskyJ.LindvallC.2018Machine learning methods to extract documentation of breast cancer symptoms from electronic health recordsJournal of Pain and Symptom Management5561492149910.1016/j.jpainsymman.2018.02.01629496537Search in Google Scholar

Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. Ca A Cancer Journal for Clinicians, 61(2), 69–90.JemalA.BrayF.CenterM.M.FerlayJ.WardE.FormanD.2011Global cancer statisticsCa A Cancer Journal for Clinicians612699010.3322/caac.2010721296855Search in Google Scholar

Khairunnahar, L., Hasib, M., Rezanur, R., Islam, M., & Hosain, K. (2019). Classification of malignant and benign tissue with logistic regression. Informatics in Medicine Unlocked, 16. https://doi.org/10.1016/j.imu.2019.100189.KhairunnaharL.HasibM.RezanurR.IslamM.HosainK.2019Classification of malignant and benign tissue with logistic regressionInformatics in Medicine Unlocked16https://doi.org/10.1016/j.imu.2019.100189.10.1016/j.imu.2019.100189Search in Google Scholar

Liu, N., Qi, E., Xu, M., Gao, B., & Liu, G. (2019). A novel intelligent classification model for breast cancer diagnosis. Information Processing & Management, 56(3), 609–623.LiuN.QiE.XuM.GaoB.LiuG.2019A novel intelligent classification model for breast cancer diagnosisInformation Processing & Management56360962310.1016/j.ipm.2018.10.014Search in Google Scholar

Mammographic Mass Dataset. Retrieved from http://archive.ics.uci.edu/ml/datasets/mammographic+mass.Mammographic Mass DatasetRetrieved from http://archive.ics.uci.edu/ml/datasets/mammographic+mass.Search in Google Scholar

Maldonado, S., Pérez, J., Weber, R., & Labbé, M. (2014). Feature selection for support vector machines via mixed integer linear programming. Information Sciences, 279, 163–175.MaldonadoS.PérezJ.WeberR.LabbéM.2014Feature selection for support vector machines via mixed integer linear programmingInformation Sciences27916317510.1016/j.ins.2014.03.110Search in Google Scholar

Mustafa, M., Nornazirah, A., Salih, F.M., Illzam, E., Suleiman, M., & Sharifa, A. (2016). Breast cancer: Detection markers, prognosis, and prevention. IOSR Journal of Dental and Medical ences, 15(8), 73–80.MustafaM.NornazirahA.SalihF.M.IllzamE.SuleimanM.SharifaA.2016Breast cancer: Detection markers, prognosis, and preventionIOSR Journal of Dental and Medical ences158738010.9790/0853-1508117380Search in Google Scholar

Noske, A., Anders, S., Ettl, J., Hapfelmeier, A., Steiger, K., Specht, K., Weichert, W., Kiechle, M., & Klein, E. (2020). Risk stratification in luminal-type breast cancer: Comparison of Ki-67 with EndoPredict test results. The Breast, 49, 101–107.NoskeA.AndersS.EttlJ.HapfelmeierA.SteigerK.SpechtK.WeichertW.KiechleM.KleinE.2020Risk stratification in luminal-type breast cancer: Comparison of Ki-67 with EndoPredict test resultsThe Breast4910110710.1016/j.breast.2019.11.004Search in Google Scholar

Quinlan, J. (1996). Improved Use of Continuous Attributes in C4.5. Journal of Artifitial Intelligence Research, 4(1), 77–90.QuinlanJ.1996Improved Use of Continuous Attributes in C4.5Journal of Artifitial Intelligence Research41779010.1613/jair.279Search in Google Scholar

Salama, G., Abdelhalim, M., & Zeid, M. (2012). Breast cancer diagnosis on three different datasets using multi-classifiers. 1(1), 8.SalamaG.AbdelhalimM.ZeidM.2012Breast cancer diagnosis on three different datasets using multi-classifiers118Search in Google Scholar

Setiono, R. (2000). Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence Medicine, 18(3), 205–219.SetionoR.2000Generating concise and accurate classification rules for breast cancer diagnosisArtificial Intelligence Medicine18320521910.1016/S0933-3657(99)00041-XSearch in Google Scholar

Siegel, R.L., Miller, K.D., & Jemal, A. (2015). Cancer statistics, 2015. Ca A Cancer Journal for Clinicians, 65(1), 5–29.SiegelR.L.MillerK.D.JemalA.2015Cancer statistics, 2015Ca A Cancer Journal for Clinicians65152910.3322/caac.2125425559415Search in Google Scholar

Singh, B. (2019). Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigm. Biocybernetics and Biomedical Engineering, 39(2), 393–409.SinghB.2019Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigmBiocybernetics and Biomedical Engineering39239340910.1016/j.bbe.2019.03.001Search in Google Scholar

Ting, F., Tan, Y., & Sim, K. (2019). Convolutional neural network improvement for breast cancer classification. Expert Systems with Applications, 120, 103–115.TingF.TanY.SimK.2019Convolutional neural network improvement for breast cancer classificationExpert Systems with Applications12010311510.1016/j.eswa.2018.11.008Search in Google Scholar

Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders. Medical Hypotheses, 135. https://doi.org/10.1016/j.mehy.2019.109503.ToğaçarM.ErgenB.CömertZ.2020Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencodersMedical Hypotheses135https://doi.org/10.1016/j.mehy.2019.109503.10.1016/j.mehy.2019.10950331760247Search in Google Scholar

Trieu, Ph., Tapia, K., Frazer, H., Lee, W., & Brennan, P. (2019). Improvement of cancer detection on mammograms via BREAST test sets. Academic Radiology, 26(12), e341–e347.TrieuPh.TapiaK.FrazerH.LeeW.BrennanP.2019Improvement of cancer detection on mammograms via BREAST test setsAcademic Radiology2612e341e34710.1016/j.acra.2018.12.01730826148Search in Google Scholar

Vrigazova, B., & Ivanov, I. (2019). Optimization of the ANOVA procedure for support vector machines. International Journal of Recent Technology and Engineering, 8(4), 5160–5165.VrigazovaB.IvanovI.2019Optimization of the ANOVA procedure for support vector machinesInternational Journal of Recent Technology and Engineering845160516510.35940/ijrte.D7375.118419Search in Google Scholar

Wang, H., Zheng, B., Yoon, S., & Ko, H. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), 687–699.WangH.ZhengB.YoonS.KoH.2018A support vector machine-based ensemble algorithm for breast cancer diagnosisEuropean Journal of Operational Research267268769910.1016/j.ejor.2017.12.001Search in Google Scholar

Wang, P., Song, Q., Li, Y., Lv, Sh., Wang, J., Li, L., & Zhang, H. (2020). Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomedical Signal Processing and Control, 57. https://doi.org/10.1016/j.bspc.2019.101789.WangP.SongQ.LiY.LvSh.WangJ.LiL.ZhangH.2020Cross-task extreme learning machine for breast cancer image classification with deep convolutional featuresBiomedical Signal Processing and Control57https://doi.org/10.1016/j.bspc.2019.101789.10.1016/j.bspc.2019.101789Search in Google Scholar

Wang, S., Wang, Y., Wang, D., Yin, Y., Wang, Y., & Jin, Y. (2019). An improved random forest-based rule extraction method for breast cancer diagnosis. Applied Soft Computing, 86. https://doi.org/10.1016/j.asoc.2019.105941.WangS.WangY.WangD.YinY.WangY.JinY.2019An improved random forest-based rule extraction method for breast cancer diagnosisApplied Soft Computing86https://doi.org/10.1016/j.asoc.2019.105941.10.1016/j.asoc.2019.105941Search in Google Scholar

Wisconsin Diagnostic Breast Cancer Dataset. Retrieved from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).Wisconsin Diagnostic Breast Cancer DatasetRetrieved from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).Search in Google Scholar

Wisconsin Prognostic Breast Cancer Dataset. Retrieved from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Prognostic).Wisconsin Prognostic Breast Cancer DatasetRetrieved from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Prognostic).Search in Google Scholar

Wu, M., Zhong, X., Peng, Q., Xu, M., Huang, S., Yuan, J., Ma, J., & Tan, T. (2019). Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting. European Journal of Radiology, 114, 175–184.WuM.ZhongX.PengQ.XuM.HuangS.YuanJ.MaJ.TanT.2019Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging settingEuropean Journal of Radiology11417518410.1016/j.ejrad.2019.03.01531005170Search in Google Scholar

Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., Rao, X., Zheng, C., & Zhang, F. (2019). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 1733, 52–60.YanR.RenF.WangZ.WangL.ZhangT.LiuY.RaoX.ZhengC.ZhangF.2019Breast cancer histopathological image classification using a hybrid deep neural networkMethods1733526010.1016/j.ymeth.2019.06.01431212016Search in Google Scholar

Zhang, X., Zhang, Y., Zhang, Q., Ren, Y., Qiu, T., Ma, T., & Sun, Q. (2019). Extracting comprehensive clinical information for breast cancer using deep learning methods. International Journal of Medical Informatics, 132. https://doi.org/10.1016/j.ijmedinf.2019.103985.ZhangX.ZhangY.ZhangQ.RenY.QiuT.MaT.SunQ.2019Extracting comprehensive clinical information for breast cancer using deep learning methodsInternational Journal of Medical Informatics132https://doi.org/10.1016/j.ijmedinf.2019.103985.10.1016/j.ijmedinf.2019.10398531627032Search in Google Scholar

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Language:
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Computer Sciences, Information Technology, Project Management, Databases and Data Mining