Cancer Deep Learning Model
This model is trained on 497 training examples and is tested for accuracy on 151 different testing examples. The accuracy is about 97%. The Python example code provides a simple example of using CSV data files with TensorFlow and training a model with three hidden layers.
I assume that you have Keras and TensorFlow installed.
Uses the Integrated Variants library to explain predictions made by a trained model
Please read this excellent paper by Mukund Sundararajan, Ankur Taly, and Qiqi Yan. When making a prediction, you can get a scaling of which input features most contributed to a classification made by the model.
A version of this code was used in a book I wrote
The github repository for my book “Introduction to Cognitive Computing” contains an older version of this example.
University of Wisconsin Cancer Data
- 0 Clump Thickness 1 - 10 - 1 Uniformity of Cell Size 1 - 10 - 2 Uniformity of Cell Shape 1 - 10 - 3 Marginal Adhesion 1 - 10 - 4 Single Epithelial Cell Size 1 - 10 - 5 Bare Nuclei 1 - 10 - 6 Bland Chromatin 1 - 10 - 7 Normal Nucleoli 1 - 10 - 8 Mitoses 1 - 10 - 9 Class (0 for benign, 1 for malignant)
I modified the original data slightly by removing the randomized patient ID and changing the target class values from (2,4) to (0,1) for (no cancer, cancer).
Licence: Apache License 2.0
Author: Mark Watson