Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse

Asian Pacific Journal of Cancer Prevention

Volumn 15, Issue 14, 2014, Pages 5883-5888

  Faradmal, Javad ^a   Soltanian, Ali Reza ^a   Roshanaei, Ghodratollah ^a   Khodabakhshi, Reza ^b   Kasaeian, Amir ^a,c  

^a HAMADAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b HAMADAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c TEHRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Artificial neural networks; Breast cancer; Disease free; Log logistic regression; Prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BREAST TUMOR; COHORT ANALYSIS; FEMALE; HUMAN; MIDDLE AGED; MORTALITY; NEOPLASM RECURRENCE, LOCAL; PATHOLOGY; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL;

BREAST NEOPLASMS; COHORT STUDIES; FEMALE; HUMANS; LOGISTIC MODELS; MIDDLE AGED; NEOPLASM RECURRENCE, LOCAL; NEURAL NETWORKS (COMPUTER); PROGNOSIS; ROC CURVE;

EID: 84905981145     PISSN: 15137368     EISSN: None     Source Type: Journal    
DOI: 10.7314/APJCP.2014.15.14.5883     Document Type: Article

References (33)

1. AJCC (2002). American Joint Committee on Cancer Staging Manual. sixth ed. New York: Springer.
2. Chi CL, Street WN, Wolberg WH (2007). Application of artificial neural network-based survival analysis on two breast cancer datasets. AMIA Annu Symp Proc, 130-4.
3. Chi CL, Street WN, Wolberg WH (2007). Application of artificial neural network-based survival analysis on two breast cancer datasets. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium, 130-4.
4. Dolgobrodov SG, Moore P, Marshall R, et al. (2007). Artificial neural network: predicted vs observed survival in patients with colonic cancer. Dis Colon Rectum, 50, 184-91.
5. Dheeba J, Selvi ST (2011). A swarm optimized neural network system for classification of microcalcification in mammograms. J Med Syst, 36, 3051-61.
6. Eleuteri A, Aung MS, Taktak AF, Damato B, Lisboa PJ (2007). Continuous and discrete time survival analysis: neural network approaches. Conf Proc IEEE Eng Med Biol Soc, 2007, 5420-3.
7. Faradmal J, Kazemnejad A, Khodabakhshi R, Gohari MR, Hajizadeh E (2010). Comparison of three adjuvant chemotherapy regimes using an extended log-logistic model in women with operable breast cancer. Asian Pac J Cancer Prev, 11, 353-8.
8. Giordano A, Giuliano M, De Laurentiis M, et al. (2011). Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients. Breast Cancer Res Treat, 129, 451-8.
9. Gohari MR, Biglarian A, Bakhshi E, Pourhoseingholi MA (2011). Use of an artificial neural network to determine prognostic factors in colorectal cancer patients. Asian Pac J Cancer Prev, 12, 1469-72.
10. Hashemian AH, Beiranvand B, Rezaei M, Bardideh A, Zand- Karimi E. (2013). Comparison of artificial neural networks and cox regression models in prediction of kidney transplant survival. Int J Adv Biol Biom Res, 1, 1204-12.
11. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G (2008). A comparative study of survival models for breast cancer prognostication on microarray data: does a single gene beat them all? Bioinformatics, 24, 2200-8.
12. Jemal A, Bray F, Center MM, et al. (2011). Global cancer statistics. CA Cancer J Clin, 61, 69-90.
13. Jerez JM, Franco L, Alba E, et al. (2005). Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks. Breast Cancer Res Tr, 94, 65-72.
14. Jang KH, Yoo TK, Choi JY, et al. (2011). Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine. Conf Proc IEEE Eng Med Biol Soc, 2011, 91-4.
15. Kleinbaum DG, Klein M (2005). Survival analysis: A self learning text. Second ed. New York: Springer.
16. Kim SM, Han H, Park JM, et al. (2012). A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability. J Digit Imaging, 25, 599-606.
17. Kurt I, Ture M, Kurum AT (2008). Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Systems with Applications, 34, 366-74.
18. Kazemnejad A, Batvandi Z, Faradmal J (2010). Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes. Eastern Mediterranean Health J, 16, 15-20.
19. Lancashire LJ, Rees RC, Ball GR (2008). Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. Artif Intell Med, 43, 99-111.
20. Naghibi S, Teshnehlab M, Shoorehdeli MA (2011). Breast cancer classification based on advanced multi dimensional fuzzy neural network. J Med Syst, 36, 2713-20.
21. National Cancer Institute (2012). Cancer terms. URL: http:// training.seer.cancer.gov/disease/cancer/terms.html.
22. Parsaeian M, Mohammad K, Mahmoudi M, Zeraati H (2012). Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey. Iranian J Publ Health, 41, 86-92.
23. Royston P (2001). The lognormal distribution as a model for survival time in cancer, with an emphasis on prognostic factors. Stat Neerlandica, 55, 89-104.
24. Ripley RM, Harris AL, Tarassenko L (2004). Non-linear survival analysis using neural networks. Stat Med, 23, 25-42.
25. Rughani AI, Dumont TM, Lu Z, et al. (2010). Use of an artificial neural network to predict head injury outcome. J Neurosurg, 113, 585-90.
26. R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria.
27. Biglarian A, Bakhshi E, Gohari MR, Khodabakhshi R (2012). Artificial neural network for prediction of distant metastasis in colorectal cancer. Asian Pac J Cancer Prev, 13, 927-30.
28. Shi H-Y, Lee K-T, Lee H-H, et al. (2012) Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS ONE, 7, 35781.
29. Tangri N, Ansell D, Naimark D (2011). Determining factors that predict technique survival on peritoneal dialysis: application of regression and artificial neural network methods. Nephron Clin Pract, 118, 93-100.
30. Uno H, Cai T, Tian L, Wei LJ (2007). Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc, 102, 527-32.
31. WHO (2012). Who cancer. URL: http://www.who.int/ mediacentre/factsheets/fs297/en/.
32. Xiang A, Lapuerta P, Ryutov A, Buckley J, Azen S (2000). Comparison of the performance of neural network methods and cox regression for censored survival data. Comput Stat Data An, 34, 243-57.
33. Zhu L, Luo W, Su M, et al. (2013). Comparison between artificial neural network and cox regression model in predicting the survival rate of gastric cancer patients. Biomedical Reports, 1, 757-60.