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From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.<\/jats:p>","DOI":"10.1155\/2010\/309841","type":"journal-article","created":{"date-parts":[[2010,8,23]],"date-time":"2010-08-23T18:34:04Z","timestamp":1282588444000},"page":"1-11","source":"Crossref","is-referenced-by-count":16,"title":["Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes"],"prefix":"10.1155","volume":"2010","author":[{"given":"Val\u00e9rie","family":"Bourd\u00e8s","sequence":"first","affiliation":[{"name":"THEMIS, 60 avenue Rockefeller, 69 008 LYON, France"},{"name":"ICTA-PM, 11 rue du Bocage, 21 121 Fontaine les Dijon, 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