Predicting Breast Cancer Survivability Using Data Mining Techniques

1 Citation

Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., & Salehi, M., Predicting breast cancer survivability using data mining techniques, 2010, 2nd International Conference on Software Technology and Engineering, 2, V2-227.

Permanent link to the paper

2 Summary

"The performance of ... self organizing map (SOM), radial basis function network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are tested both on the Wisconsin breast cancer data (WBCD) and on the Shiraz Namazi Hospital breast cancer data (NHBCD)."

3 Study outcomes

List the paper results concerning method comparison and benchmarking:

3.1 Outcome O1

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

3.2 Outcome O2

...

Outcome O2 is presented as Figure X in the original publication.

3.3 Outcome On

...

Outcome On is presented as Figure X in the original publication.

3.4 Further outcomes

If intended, you can add further outcomes here.

4 Study design and evidence level

4.1 General aspects

You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:

4.2 Design for Outcome O1

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

4.3 Design for Outcome O2

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

...

4.4 Design for Outcome O

• The outcome was generated for ...
• Configuration parameters were chosen ...
• ...

6 References

The list of cited or related literature is placed here.