Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Computational Statistics & Data Analysis
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Stratification of Severity of Illness Indices: A Case Study for Breast Cancer Prognosis
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Evaluation of missing data imputation in longitudinal cohort studies in breast cancer survival
International Journal of Knowledge Engineering and Soft Data Paradigms
IEEE Transactions on Neural Networks
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Towards the integration of a bioprofile in ocular melanoma
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
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Accurate modelling of time-to-event data is of particular importance for both exploratory and predictive analysis in cancer, and can have a direct impact on clinical care. This study presents a detailed double-blind evaluation of the accuracy in out-of-sample prediction of mortality from two generic non-linear models, using artificial neural networks benchmarked against a partial logistic spline, log-normal and COX regression models. A data set containing 2880 samples was shared over the Internet using a purpose-built secure environment called GEOCONDA (www.geoconda.com). The evaluation was carried out in three parts. The first was a comparison between the predicted survival estimates for each of the four survival groups defined by the TNM staging system, against the empirical estimates derived by the Kaplan-Meier method. The second approach focused on the accurate prediction of survival over time, quantified with the time dependent C index (C^t^d). Finally, calibration plots were obtained over the range of follow-up and tested using a generalization of the Hosmer-Lemeshow test. All models showed satisfactory performance, with values of C^t^d of about 0.7. None of the models showed a systematic tendency towards over/under estimation of the observed survival at @t=3 and 5 years. At @t=10 years, all models underestimated the observed survival, except for COX regression which returned an overestimate. The study presents a robust and unbiased benchmarking methodology using a bespoke web facility. It was concluded that powerful, recent flexible modelling algorithms show a comparative predictive performance to that of more established methods from the medical and biological literature, for the reference data set.