Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/

1. Machine Learning for Breast
Cancer Diagnosis
A Proof of Concept
P. K. SHARMA
Email: from_pramod @yahoo.com

2. Introduction
 Machine learning is branch of Data Science which incorporates a large set of statistical techniques.
 These techniques enable data scientists to create a model which can learn from past data and detect
patterns from massive, noisy and complex data sets.
 Researchers use machine learning for cancer prediction and prognosis.
 Machine learning allows inferences or decisions that otherwise cannot be made using conventional
statistical methodologies.
 With a robustly validated machine learning model, chances of right diagnosis improve.
 It specially helps in interpretation of results for borderline cases.

3. Breast Cancer: An overview
 The most common cancer in women worldwide.
 The principle cause of death from cancer among women globally.
 Early detection is the most effective way to reduce breast cancer deaths.
 Early diagnosis requires an accurate and reliable procedure to distinguish between benign breast tumors
from malignant ones
 Breast Cancer Types - three types of breast tumors: Benign breast tumors, In-situ cancers, and Invasive
cancers.
 The majority of breast tumors detected by mammography are benign.
 They are non-cancerous growths and cannot spread outside of the breast to other organs.
 In some cases, it is difficult to distinguish certain benign masses from malignant lesions with mammography.
 If the malignant cells have not gone through the basal membrane but is completely contained in the lobule or the
ducts, the cancer is called in-situ or noninvasive.
 If the cancer has broken through the basal membrane and spread into the surrounding tissue, it is called invasive.
 This analysis assists in differentiating between benign and malignant tumors.

4. Data Source
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
 The data used for this POC is from University of
Wisconsin.
 Citation: This breast cancer databases was obtained from
the University of Wisconsin Hospitals, Madison from Dr.
William H. Wolberg.
 Reference :
o O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via
linear programming", SIAM News, Volume 23, Number 5,
September 1990, pp 1 & 18.
o William H. Wolberg and O.L. Mangasarian: "Multisurface
method of pattern separation for medical diagnosis applied to
breast cytology", Proceedings of the National Academy of
Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196.
o O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern
recognition via linear programming: Theory and application to
medical diagnosis", in: "Large-scale numerical optimization",
Thomas F. Coleman and Yuying Li, editors, SIAM Publications,
Philadelphia 1990, pp 22-30.
o K. P. Bennett & O. L. Mangasarian: "Robust linear programming
discrimination of two linearly inseparable sets", Optimization
Methods and Software 1, 1992, 23-34 (Gordon & Breach Science
Publishers).

5. Data Files
Data File Name Description File Name # of records
# of
attributes
breast-cancer-wisconsin.data breast-cancer-wisconsin.names 699 11
unformatted-data
Data file with comments based on
breast-cancer-wisconsin.data
699 11
wdbc.data wdbc.names 569 32
wpbc.data wpbc.names 198 34
In this case study, lets analyze breast-cancer-wisconsin.data and wdbc.data.

6. Data Sets
The data is in CSV format without any column headers. Columns are interpreted from the associated “names”
files.

7. Flow of Data
Biopsy
Procedure
Measurements Reports Evaluation Diagnosis
Analysis of
measurements
Preparation of
ML Models
Predictions and
validation

8. Analysis
Classifier Params.
 min_samples_leaf
 n_estimators
 min_samples_split
 max_features
Data Preparation
 Address missing data
 Training - Testing –
Validation data
Lab Setup
Components
Libraries
EnvironmentPython
scikit-learn
RandomForestClassifier
Linux
PandasSciPy
NumPy IPython Matplotlib seaborn
StratifiedKFold
train_test_split
GridSearchCV
learning_curve
pyplot
interp
Input Files
 wdbc.data
 breast-cancer-
wisconsin.data
Outputs
 Trained Classifier
 Predictions

9. Data Visualization

10. Data Description : wdbc.data
1. ID number
2. Diagnosis (M = malignant, B = benign)
3-32. Ten real-valued features are computed for
each cell nucleus:
a) radius (mean of distances from center to
points on the perimeter)
b) texture (standard deviation of gray-scale
values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the
contour)
h) concave points (number of concave portions
of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" -
1)
 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.
 The mean, standard error, and "worst" or largest (mean of the three largest
values) of these features were computed for each image, resulting in 30
features.
 For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.
All feature values are recoded with four significant digits.

11. wdbc.data
 Mean Radius, Mean Perimeter and Mean appear to be helpful
in classification.
 Higher the values of each parameter more are the chances of it
being malignant.

12. wdbc.data
 Mean Concavity, Mean Concave Points, and Mean
Compactness appear to be helpful in classification.
 Higher the values of each parameter more are the
chances of it being malignant.

13. wdbc.data
 Mean Smoothness,
Mean Texture,
Mean Fractal
Dimension, Mean
Symmetry and
Mean
Compactness do
not appears to
have influence on
classification.
 Both type of cases
are spread across.

14. Data Description : breast-cancer-wisconsin.data
 Missing attribute values: 16
 There are 16 instances in Groups 1 to 6 that
contain a single missing (i.e., unavailable)
attribute value, now denoted by "?".
# Attribute Domain
1. Sample code number id number
2. Clump Thickness 1 - 10
3. Uniformity of Cell Size 1 - 10
4. Uniformity of Cell Shape 1 - 10
5. Marginal Adhesion 1 - 10
6. Single Epithelial Cell Size 1 - 10
7. Bare Nuclei 1 - 10
8. Bland Chromatin 1 - 10
9. Normal Nucleoli 1 - 10
10. Mitoses 1 - 10
11. Class
(2 for benign, 4
for malignant)

15. breast-cancer-wisconsin.data
 The features distinguish between
benign and Malignant fairly well.

16. breast-cancer-wisconsin.data
 The feature seems to distinguish between
benign and Malignant fairly well.

17. breast-cancer-wisconsin.data
 The feature seems to distinguish
between benign and Malignant fairly
well.

18. Results
WDBC.DATA

19. Analysis: wdbc.data
 Training data is divided in 5 folds.
 Test data has 114 records
 Accuracy Score: 0.9561
Confusion Matrix: Predicted Benign Predicted Malignant
True Benign 69 2
True Malignant 3 40
Classification
Report:
Precision Recall f1-score Support
0 0.96 0.97 0.97 71
1 0.95 0.93 0.94 43
avg / total 0.96 0.96 0.96 114
Three cases, although
malignant, are predicted
as benign
• High accuracy.
• Supports the diagnosis.
Model performs equally
well on both test and
training sets
Two dimensional plot shows
excellent separation of
Benign and Malignant cases

20. Plotting three cases…
Factors influencing predictions.

21. Plotting three cases:
Factors having no influence on predictions…

22. Plotting two features at a time
 Also analyzed cases if only two of the
features were available.
 Classifier was trained on two features at
a time and decision boundary is
plotted.
 Model could predict the cases with
reasonable accuracy

23. Results
BREAST-CANCER-WISCONSIN.DATA

24. Analysis:
breast-cancer-wisconsin.data
 Training data is divided in 5 folds.
 Test data has 140 records
 Accuracy Score: 0.9643
Confusion Matrix: Predicted Benign Predicted Malignant
True Benign 92 3
True Malignant 2 43
Classification
Report:
Precision Recall f1-score Support
0 0.98 0.97 0.97 95
1 0.93 0.96 0.95 45
avg / total 0.96 0.96 0.96 140
Two cases, although
malignant, are predicted
as benign
Model performs equally
well on both training as
well as test data
• High accuracy.
• Supports the diagnosis.
Two dimensional plot shows excellent
separation of Benign and Malignant cases

25. Plotting two cases…

26. Plotting three cases…
Factors influencing predictions.

27. Plotting two features at a time
 Classifier was trained
on two features at a
time and decision
boundary is plotted.
 As expected, classifier
needs more than just
two parameters to
give accurate
predictions.