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https://github.com/mark-watson/cancer-deep-learning-model
Keras Deep Learning neural network model for University of Wisconsin Cancer data that uses the Integrated Variants library to explain predictions made by a trained model
https://github.com/mark-watson/cancer-deep-learning-model
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Keras Deep Learning neural network model for University of Wisconsin Cancer data that uses the Integrated Variants library to explain predictions made by a trained model
- Host: GitHub
- URL: https://github.com/mark-watson/cancer-deep-learning-model
- Owner: mark-watson
- License: apache-2.0
- Created: 2016-07-15T22:04:29.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-06-13T19:21:30.000Z (over 4 years ago)
- Last Synced: 2024-08-05T10:08:20.481Z (5 months ago)
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 72
- Watchers: 18
- Forks: 47
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ai-cancer - mark-watson/cancer-deep-learning-model - Keras deep Learning neural network model for University of Wisconsin Cancer data that uses the Integrated Variants library to explain predictions made by a trained model with 97% accuracy (Code / Repositories)
README
# Keras Deep Neural Network using Breast Cancer Data with Explanation of Predictions
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.
## Donate on Patreon to support all of my projects
Please visit [https://www.patreon.com/markwatson](https://www.patreon.com/markwatson) and sign up to donate $1/month
## Uses the IntegratedVarients library to explain predictions made by a trained model
Please [read this excellent paper](https://arxiv.org/pdf/1703.01365.pdf)
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 classifiaction made by the model.
For example:
````````
** Contributions to classification for sample type benign sample **
Clump Thickness : -15
Uniformity of Cell Size : 19
Uniformity of Cell Shape : -5
Marginal Adhesion : -15
Single Epithelial Cell Size : -100
Bare Nuclei : -5
Bland Chromatin : -70
Normal Nucleoli : -5
Mitoses : 9
** Contributions to classification for sample type malignant sample **
Clump Thickness : 27
Uniformity of Cell Size : 8
Uniformity of Cell Shape : 15
Marginal Adhesion : -21
Single Epithelial Cell Size : -8
Bare Nuclei : 100
Bland Chromatin : 20
Normal Nucleoli : 5
Mitoses : 3
````````
## A version of this code was used in a book I wrote
The [github repository for my book "Introduction to Cognitive Computing"](https://github.com/mark-watson/cognitive-computing-book)
contains an older version of this example.
# Universary of Wisconcin 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).
The CSV file loader in the TensorFlow contrib learn library expects header lines. The following is the first few lines of train.csv:
````````
10,10,10,8,6,1,8,9,1,1
6,2,1,1,1,1,7,1,1,0
2,5,3,3,6,7,7,5,1,1
````````
The last value on each input line is 0 or 1 indicating the target classification.
This example just has 2 target classifications, but you can have any number. Label target class values 0, 1, 2, etc.