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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 diagnosis831064106910.1016/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 ensemble4952061207610.1007/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 syndrome219343043310.1016/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 survivability311023Search in Google Scholar
Cortes, C., & Vapnik V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.CortesC.VapnikV.1995Support-vector networks20327329710.1007/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 process34114164417210.1118/1.278686418072480Search 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 records5561492149910.1016/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 statistics612699010.3322/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 regression16https://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 diagnosis56360962310.1016/j.ipm.2018.10.014Search in Google Scholar
Mammographic Mass Dataset. Retrieved from http://archive.ics.uci.edu/ml/datasets/mammographic+mass.Retrieved 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 programming27916317510.1016/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 prevention158738010.9790/0853-1508117380Search 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 results4910110710.1016/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 C4.541779010.1613/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.2012118Search 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 diagnosis18320521910.1016/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, 201565152910.3322/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 paradigm39239340910.1016/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 classification12010311510.1016/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 autoencoders135https://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 sets2612e341e34710.1016/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 machines845160516510.35940/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 diagnosis267268769910.1016/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 features57https://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 diagnosis86https://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).Retrieved 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).Retrieved 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 setting11417518410.1016/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 network1733526010.1016/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 methods132https://doi.org/10.1016/j.ijmedinf.2019.103985.10.1016/j.ijmedinf.2019.10398531627032Search in Google Scholar