Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Mining microarray data to predict the histological grade of a breast cancer
Journal of Biomedical Informatics
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
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This paper deals with the problem of learning prognostic models from medical survival data, where the sole prediction of probability of event (and not its probability dependency on time) is of interest. To appropriately consider the follow-up time and censoring -- both characteristic for survival data -- we propose a weighting technique that lessens the impact of data from patients for which the event did not occur and have short follow-up times. A case study on prostate cancer recurrence shows that by incorporating this weighting technique the machine learning tools stand beside or even outperform modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data.