The first 30 features are computed from a digitized image of a
fine needle aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image. There are two possible learning problems: predicting status
or predicting
the time to recur.
1) Predicting field 2, outcome: R = recurrent, N = non-recurrent
- Dataset should first be filtered to reflect a particular
endpoint; e.g., recurrences before 24 months = positive,
non-recurrence beyond 24 months = negative.
- 86.3 previous version of this data.
2) Predicting Time To Recur (field 3 in recurrent records)
- Estimated mean error 13.9 months using Recurrence Surface
Approximation.
The data are originally available from the UCI machine learning repository, see
http://www.ics.uci.edu/~mlearn/databases/breast-cancer-wisconsin/.