The Wisconsin Breast Cancer Problem : Diagnosis and DFS time prognosis using probabilistic and generalised regression neural classifiers

This papers deals with the breast cancer diagnosis and prognosis problem employing two proposed neural network architectures over the Wisconsin Diagnostic and Prognostic Breast Cancer (WDBC/WPBC) datasets. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among instances derived from the Fine Needle Aspirate (FNA) test, while the second architecture estimates the time interval that possibly contain the right end-point of the patient’s Disease-Free Survival (DFS) time. The accuracy of the neural classifiers reaches nearly 98% for the diagnosis and 92% for the prognosis problem. Furthermore, the prognostic recurrence predictions were further evaluated using survival analysis through the Kaplan-Meier approximation method and compared with other techniques from the literature.

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