Fol. Biol. 2019, 65, 212-220
https://doi.org/10.14712/fb2019065050212
Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data
References
1. 2013) Survival rate of breast cancer patients in Malaysia: a population-based study. Asian Pac. J. Cancer Prev. 14, 4591-4594.
< , N. A., Wan Mahiyuddin, W. R., Muhammad, N. A., Ali, Z. M., Ibrahim, L., Ibrahim Tamim, N. S., Mustafa, A. N., Kamaluddin, M. A. (https://doi.org/10.7314/APJCP.2013.14.8.4591>
2. Acuña, E., Rodriguez, C. (2004) The treatment of missing values and its effect on classifier accuracy. In: Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation, eds. Banks, D., House, L., McMorris, F. R., Arabie, P., Gaul, W. pp. 639-647. Springer Berlin, Heidelberg.
3. 2016) Deep learning for computational biology. Mol. Syst. Biol. 12, 878.
< , C., Pärnamaa, T., Parts, L., Stegle, O. (https://doi.org/10.15252/msb.20156651>
4. 2008) Path similarity skeleton graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1282-1292.
, X., Latecki, L. J. (
5. 2018) Big data and machine learning in health care. JAMA, 319, 1317-1318.
< , A. L., Kohane, I. S. (https://doi.org/10.1001/jama.2017.18391>
6. 2017) Classification of highgrade glioma into tumor and nontumor components using support vector machine. Am. J. Neuroradiol. 38, 908-914.
< , D. T., Artzi, M., Liberman, G., Bokstein, F., Aizenstein, O., Bashat, D. B. (https://doi.org/10.3174/ajnr.A5127>
7. 2016) Model comparison for breast cancer prognosis based on clinical data. PLoS One, 11, e0146413.
< , S., Al-Ali, R., Elkum, N. (https://doi.org/10.1371/journal.pone.0146413>
8. 2001). Random forests. Mach. Learn. 45, 5-32.
< , L. (https://doi.org/10.1023/A:1010933404324>
9. 2015) Time trends in breast cancer among Indian women population: an analysis of population based cancer registry data. Indian J. Surg. Oncol. 6, 427-434.
< , M., Vaitheeswaran, K., Satishkumar, K., Das, P., Stephen, S., Nandakumar, A. (https://doi.org/10.1007/s13193-015-0467-z>
10. 2005) Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34, 113-127.
< , D., Walker, G., Kadam, A. (https://doi.org/10.1016/j.artmed.2004.07.002>
11. 2012) A few useful things to know about machine learning. Commun. ACM, 55, 78-87.
< , P. (https://doi.org/10.1145/2347736.2347755>
12. 2019) Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med. Inform. Decis. Mak. 19, 48.
< , M. D., Taib, N. A., Har, Y. C., Lio, P., Dhillon, S. K. (https://doi.org/10.1186/s12911-019-0801-4>
13. 2008) A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes. Trials 9, 51.
< , S., Maclennan, G., Cook, J. A., Ramsay, C. R. (https://doi.org/10.1186/1745-6215-9-51>
14. Ghosh, R., Papapanagiotou, I., Boloor, K. (2014) A survey on research initiatives for healthcare clouds. In: Cloud Computing Applications for Quality Health Care Delivery, eds. Moumtzoglu, A., Kastania, A., IGI Global, pp. 1-18. Hershey, PA.
15. Han, J., Kamber, M. (2000) Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA.
16. 2009) Comparison of time trends in breast cancer incidence (1973-2002) in Asia, from cancer incidence in five continents, Vols IV-IX. Jap. J. Clin. Oncol. 39, 411-412.
< , Y., Zhang, M. (https://doi.org/10.1093/jjco/hyp054>
17. 2014) Multivariate pattern analysis of fMRI in breast cancer survivors and healthy women. J. Int. Neuropsychol. Soc. 20, 391-401.
< , S. M. H., Kesler, S. R. (https://doi.org/10.1017/S1355617713001173>
18. 2008) Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst. Appl. 34, 578-587.
< , C.-L., Liao, H.-C., Chen, M.-C. (https://doi.org/10.1016/j.eswa.2006.09.041>
19. 2006) A support vector machinebased method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli. Bioinformatics 22, 278-284.
< , S., Kulkarni, A. J., Kulkarni, B. D., Jayaraman, V. K., Balaji, P. V. (https://doi.org/10.1093/bioinformatics/bti810>
20. 2013) Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data. J. Am. Med. Inform. Assoc. 20, 613-618.
< , J., Shin, H. (https://doi.org/10.1136/amiajnl-2012-001570>
21. 2017) Advanced stage at presentation remains a major factor contributing to breast cancer survival disparity between public and private hospitals in a middle-income country. Int. J. Environ. Res. Public Health 14, 326-453.
< , Y.-C., Bhoo-Pathy, N., Subramaniam, S., Bhoo-Pathy, N., Taib, N. A., Jamaris, S., Kaur, K., See, M. H., Ho, G. F., Yip, C. H. (https://doi.org/10.3390/ijerph14040427>
22. 2017) Deep convolutional neural networks for imaging based survival analysis of rectal cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 99, S183.
< , H., Zhong, H., Boimel, P. J., Ben-Josef, E., Xiao, Y., Fan, Y. (https://doi.org/10.1016/j.ijrobp.2017.06.458>
23. 2015) Prediction of breast cancer survival through knowledge discovery in databases. Glob. J. Health Sci. 7, 392-398.
Afshar, H., Ahmadi, M., Roudbari, M., Sadoughi, F. (
24. 2018) Survival time and prognostic factors for breast cancer among women in north-east peninsular Malaysia. Asian Pac. J. Cancer Prev. 19, 497-502.
, N., Yaacob, N. M., Abdullah, N. H., Hairon, S. M. (
25. 2013) Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 26, 2194-2205.
< , K., Ali, A., Kim, D., An, Y., Kim, M., Shin, H. (https://doi.org/10.1016/j.engappai.2013.06.013>
26. 2018) A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv., 51, 92.
, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Shu C. C.,C., Iyengar, S. S. (
27. 2011) Evaluation: from precision, recall and f-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Tech. 2, 37-63.
, D. M. (
28. 2007) Brain MRI slices classification using least squares support vector machine. ICMED 1, 21–33.
, H., Selvi, S. T., Selvathi, D., Gewali, L. (
29. 2017). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J. Biomed. Health Inform. 1, 1.
, B., Tighe, P., Bihorac, A., & Rashidi, P. (
30. 2018) Breast cancer data analysis for survivability studies and prediction. Comput. Methods Programs Biomed. 155, 199-208.
< , N., Hagenbuchner, M., Win, K. T., Yang, J. (https://doi.org/10.1016/j.cmpb.2017.12.011>
31. 2006) Ethnic differences in the time trend of female breast cancer incidence: Singapore, 1968-2002. BMC Cancer 6, 261.
< , X., Ali, R. A., Wedren, S., Goh, D. L.-M., Tan, C.-S., Reilly, M., Hall, P., Chia, K. S. (https://doi.org/10.1186/1471-2407-6-261>
32. 2018) A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE Trans. Comput. Biol. Bioinform. 1, 10.
, D., Wang, M., Li, A. (
33. 2011) Improvement in survival of breast cancer patients - trends over two time periods in a single institution in an Asia Pacific country, Malaysia. Asian Pac. J. Cancer Prev. 12, 345-349.
, N. A., Akmal, M., Mohamed, I., Yip, C.-H. (
34. Vapnik, V. N. (1995) The Nature of Statistical Learning Theory. Berlin, Heidelberg: Springer-Verlag.
35. 2003) Data preparation for data mining. Applied Artificial Intelligence, 17, 375-381.
< , S., Zhang, C., Yang, Q. (https://doi.org/10.1080/713827180>