Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values

Computers in Biology and Medicine

Volumn 59, Issue , 2015, Pages 125-133

  García Laencina, Pedro J ^a   Abreu, Pedro Henriques ^b,c   Abreu, Miguel Henriques ^d   Afonoso, Noémia ^d  

^a Centro Universitario de la Defensa de San Javier   (Spain)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c UNIVERSITY OF COIMBRA   (Portugal)

^d PORTUGUESE ONCOLOGY INSTITUTE   (Portugal)

Author keywords

5 year survival prediction; Breast cancer; Discrete data; Imputation; Missing data

Indexed keywords

ARTIFICIAL INTELLIGENCE; DISEASES; FORECASTING; LEARNING SYSTEMS; MAXIMUM PRINCIPLE; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH;

BREAST CANCER; DISCRETE DATA; IMPUTATION; MISSING DATA; SURVIVAL PREDICTION;

DATA MINING;

ARTICLE; BREAST CANCER; CANCER PATIENT; CANCER SURVIVAL; CLASSIFICATION; DATA ANALYSIS; HUMAN; K NEAREST NEIGHBOR; LOGISTIC REGRESSION ANALYSIS; MEASUREMENT ACCURACY; MISSING DATA IMPUTATION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; SURVIVAL PREDICTION; SURVIVAL TIME; ADULT; AGED; ALGORITHM; BREAST TUMOR; FEMALE; MIDDLE AGED; MORTALITY; STATISTICAL MODEL; SURVIVAL; VERY ELDERLY; YOUNG ADULT;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; BREAST NEOPLASMS; FEMALE; HUMANS; MIDDLE AGED; MODELS, STATISTICAL; SURVIVAL ANALYSIS; YOUNG ADULT;

EID: 84923379438     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2015.02.006     Document Type: Article

References (45)

1. Siegel R., Ma J., Zou Z., Jemal A. Cancer statistics, 2014. Cancer J. Clin. 2014, 64(1):9-29.
2. Clark G.M., Dressler L.G., Owens M.A., Pounds G., Oldaker T., McGuire W.L. Prediction of relapse or survival in patients with node-negative breast cancer by dna flow cytometry. New Engl. J. Med. 1989, 320:627-633.
3. Delen D., Walker G., Kadam A. Predicting breast cancer survivability. a comparison of three data mining methods. Artif. Intell. Med. 2005, 34(2):113-127.
4. H. Miao, M. Hartman, N. Bhoo-Pathy, S.-C. Lee, N.A. Taib, E.-Y. Tan, P. Chan, K.G.M. Moons, H.S. Wong, J. Goh, S.M. Rahim, C.H. Yip, H.M. Verkooijen, Predicting survival of de novo metastatic breast cancer in asian women: systematic review and validation study, PLoS One 9 (4) (2014). doi:10.1371/journal.pone.0093755.
5. Burton A., Altman D.G. Missing covariate data within cancer prognostic studies. a review of current reporting and proposed guidelines. Br. J. Cancer 2004, 91:4-8.
6. Jerez J.M., Molina I., Garcia-Laencina P.J., Alba E., Ribelles N., Martin M., Franco L. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif. Intell. Med. 2010, 50(2):105-115.
7. Abreu P.H., Amaro H.A., Castro-Silva D., Machado P., Abreu M.H., Afonso N., Dourado A. Overall survival prediction for women breast cancer using ensemble methods and incomplete clinical data. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings 2014, vol. 41:1366-1369. Springer, Seville, Spain.
8. Abreu P.H., Amaro H.A., Castro-Silva D., Machado P., Abreu M.H., Afonso N., Dourado A. Personalizing breast cancer patients with heterogeneous data. International Conference on Health Informatics, IFMBE Proceedings 2014, vol. 42:39-42. Springer, Vilamoura, Portugal.
9. AJCC Cancer Staging Manual 2010, Springer, Chicago, USA. S. Edge, D. Byrd, C. Compton, A. Fritz, F. Greene, A. Trotti (Eds.).
10. Little R.J.A. Methods for handling missing values in clinical trials. J. Rheumatol. 1999, 26(8):1654-1656.
11. P.D. Allison, Missing Data, Sage University Papers Series on Quantitative Applications in the Social Sciences, Thousand Oaks, CA, USA, 2001.
12. García-Laencina P.J., Sancho-Gómez J.L., Figueiras-Vidal A. Pattern classification with missing data. a review. Neural Comput. Appl. 2010, 19:263-282.
13. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data 2002, Wiley, Hoboken, NJ. 2nd edition.
14. Cismondi F., Fialho A.S., Vieira S.M., Reti S.R., Sousa J.M., Finkelstein S.N. Missing data in medical databases. impute delete or classify?. Artif. Intell. Med. 2013, 58(1):63-72.
15. Cruz J.A., Wishart D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Informatics 2006, (2):59-77.
16. Polat K., Gunes S. Breast cancer diagnosis using least square support vector machine. Digit. Signal Process. 2007, 17(4):694-701.
17. Sahan S., Polat K., Kodaz H., Gunes S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput. Biol. Med. 2007, 37(3):415-423.
18. Daemen A., Timmerman D., den Bosch T.V., Bottomley C., Kirk E., Holsbeke C.V., Valentin L., Bourne T., Moor B.D. Improved modeling of clinical data with kernel methods. Artif. Intell. Med. 2012, 54(2):103-114.
19. Wang K.-J., Makond B., Chen K.-H., Wang K.-M. A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients. Appl. Soft Comput. 2014, 20:15-24.
20. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 1977, 39(1):1-38.
21. Z. Ghahramani, M.I. Jordan, Supervised learning from incomplete data via an EM approach, in: J.D. Cowan, G. Tesauro, J. Alspector (Eds.), Advances in Neural Information Processing Systems, vol. 6, Denver, CO, 1993, pp. 120-127.
22. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer, Cambridge, UK.
23. Zio M.D., Guarnera U., Luzi O. Imputation through finite gaussian mixture models. Comput. Stat. Data Anal. 2007, 51(11):5305-5316.
24. Train K.E. EM algorithms for nonparametric estimation of mixing distributions. J. Choice Model. 2008, 1(1):40-69.
25. Aha D.W. Lazy Learning 1997, Kluwer Academic Publishers, Norwell, MA, USA.
26. Mitchell T.M. Machine Learning 1997, McGraw-Hill, New York.
27. Batista G.E., Monard M.C. An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 2003, 17:519-533.
28. Troyanskaya O., Cantor M., Alter O., Sherlock G., Brown P., Botstein D., Tibshirani R., Hastie T., Altman R. Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525.
29. García-Laencina P.J., Sancho-Gómez J.-L., Figueiras-Vidal A.R., Verleysen M. K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing 2009, 72(7-9):1483-1493.
30. Dudani S.A. The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. 1976, 6(4):325-327.
31. Quinlan J.R. C4.5. Programs for Machine Learning 1993, Morgan Kaufmann, San Mateo, CA.
32. Kantardzic M. Data Mining. Concepts Models Methods and Algorithms 2011, Wiley-IEEE Press, New Jersey, USA. 2nd edition.
33. R. Latkowski, High computational complexity of the decision tree induction with many missing attribute values, in: Proceedings of Concurrency, Specification and Programming. CS&P, vol. 22, 2003, pp. 318-325.
34. Vapnik V.N. Statistical Learning Theory 1998, Wiley-Interscience, New York, USA.
35. Cristianini N., Shawe Taylor J. Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, Cambridge, USA.
36. Scholkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT Press, Cambridge, MA, USA.
37. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9(3):293-300.
38. Park K., Ali A., Kim D., An Y., Kim M., Shin H. Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 2013, 26(9):2194-2205.
39. Prentice R.L., Gloeckler L.A. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 1978, 34(1):57-67.
40. Burke H.B., Goodman P.H., Rosen D.B., Henson D.E., Weinstein J.N., Harrell F.E., Marks J.R., Winchester D.P., Bostwick D.G. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 1997, 79(4):857-862.
41. Markey M.K., Tourassi G.D., Margolis M., DeLong D.M. Impact of missing data in evaluating artificial neural networks trained on complete data. Comput. Biol. Med. 2006, 36(5):516-525.
42. Dorri F., Azmi P., Dorri F. Missing value imputation in DNA microarrays based on conjugate gradient method. Comput. Biol. Med. 2012, 42(2):222-227.
43. Abawajy J., Kelarev A., Chowdhury M., Stranieri A., Jelinek H.F. Predicting cardiac autonomic neuropathy category for diabetic data with missing values. Comput. Biol. Med. 2013, 43(10):1328-1333.
44. Herring A.H., Ibrahim J.G., Lipsitz S.R. Non-ignorable missing covariate data in survival analysis. a case-study of an international breast cancer study group trial. J. R. Stat. Soc. Ser. C (Appl. Stat.) 2004, 53(2):293-310.
45. J.M. Jerez, I. Molina, J.L. Subirats, L. Franco, Missing data imputation in breast cancer prognosis, in: BioMed'06: Proceedings of the 24th IASTED International Conference on Biomedical Engineering, 2006, pp. 323-328.