Breast Cancer Detection with ML¶

Using Open-source UCI repository dataset, we will train the model of breast cancer detection. K-nearest neighborhood and Support Vector Machine will be used. In this data, the goal is to predict malignant or benign based on some features.

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author: Chanseok Kang
categories: [Python, Machine_Learning]
image: images/breast_cancer.png

Required Packages¶

In [1]:

import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import sklearn

from sklearn import preprocessing
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import classification_report, accuracy_score
from pandas.plotting import scatter_matrix

Version Check¶

In [2]:

print('Python: {}'.format(sys.version))
print('Numpy: {}'.format(np.__version__))
print('Matplotlib: {}'.format(matplotlib.__version__))
print('Pandas: {}'.format(pd.__version__))
print('Scikit-learn: {}'.format(sklearn.__version__))

Python: 3.7.6 (default, Jan  8 2020, 19:59:22) 
[GCC 7.3.0]
Numpy: 1.18.1
Matplotlib: 3.1.3
Pandas: 1.0.1
Scikit-learn: 0.23.1

Dataset Load¶

More data information is in here

In [3]:

url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
names = ['id', 'clump_thickness', 'uniform_cell_size', 'uniform_cell_shape', 
         'marginal_adhesion', 'single_epithelial_size', 'bare_nuclei',
         'bland_chromatin', 'normal_nucleoli', 'mitoses', 'class']

df = pd.read_csv(url, names=names).replace('?', np.nan).dropna()

In [4]:

df.head()

Out[4]:

	id	clump_thickness	uniform_cell_size	uniform_cell_shape	marginal_adhesion	single_epithelial_size	bare_nuclei	bland_chromatin	normal_nucleoli	mitoses	class
0	1000025	5	1	1	1	2	1	3	1	1	2
1	1002945	5	4	4	5	7	10	3	2	1	2
2	1015425	3	1	1	1	2	2	3	1	1	2
3	1016277	6	8	8	1	3	4	3	7	1	2
4	1017023	4	1	1	3	2	1	3	1	1	2

Preprocess data¶

In [5]:

# Drop useless feature for classification
df.drop(['id'], axis=1, inplace=True)

Summarize Dataset¶

In [6]:

print(df.describe())

       clump_thickness  uniform_cell_size  uniform_cell_shape  \
count       683.000000         683.000000          683.000000   
mean          4.442167           3.150805            3.215227   
std           2.820761           3.065145            2.988581   
min           1.000000           1.000000            1.000000   
25%           2.000000           1.000000            1.000000   
50%           4.000000           1.000000            1.000000   
75%           6.000000           5.000000            5.000000   
max          10.000000          10.000000           10.000000   

       marginal_adhesion  single_epithelial_size  bland_chromatin  \
count         683.000000              683.000000       683.000000   
mean            2.830161                3.234261         3.445095   
std             2.864562                2.223085         2.449697   
min             1.000000                1.000000         1.000000   
25%             1.000000                2.000000         2.000000   
50%             1.000000                2.000000         3.000000   
75%             4.000000                4.000000         5.000000   
max            10.000000               10.000000        10.000000   

       normal_nucleoli     mitoses       class  
count       683.000000  683.000000  683.000000  
mean          2.869693    1.603221    2.699854  
std           3.052666    1.732674    0.954592  
min           1.000000    1.000000    2.000000  
25%           1.000000    1.000000    2.000000  
50%           1.000000    1.000000    2.000000  
75%           4.000000    1.000000    4.000000  
max          10.000000   10.000000    4.000000

In [7]:

# Plot histograms for each variable
df.hist(figsize=(10, 10));

In [8]:

# Create scatter plot matrix
scatter_matrix(df, figsize=(18, 18));

Split Train/Test data¶

In [9]:

X = df.drop(['class'], axis=1).to_numpy()
y = df['class'].to_numpy()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Specify Test options¶

In [10]:

seed = 8
scoring = 'accuracy'

Define the model to train¶

In [11]:

models = []
models.append(('KNN', KNeighborsClassifier(n_neighbors=5)))
models.append(('SVM', SVC()))

# Evaluate each model in turn
results = []
names = []

for name, model in models:
    kfold = KFold(n_splits=10, random_state=seed, shuffle=True)
    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

KNN: 0.981684 (0.020103)
SVM: 0.974310 (0.017003)

Make Predictions on validation dataset¶

In [12]:

for name, model in models:
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print(name)
    print(accuracy_score(y_test, predictions))
    print(classification_report(y_test, predictions))

KNN
0.9635036496350365
              precision    recall  f1-score   support

           2       0.97      0.98      0.97        89
           4       0.96      0.94      0.95        48

    accuracy                           0.96       137
   macro avg       0.96      0.96      0.96       137
weighted avg       0.96      0.96      0.96       137

SVM
0.9562043795620438
              precision    recall  f1-score   support

           2       0.98      0.96      0.97        89
           4       0.92      0.96      0.94        48

    accuracy                           0.96       137
   macro avg       0.95      0.96      0.95       137
weighted avg       0.96      0.96      0.96       137

Another way to get accuracy¶

In [13]:

clf = SVC()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print(accuracy)

0.9562043795620438

Test it with samples¶

In [14]:

example = np.array([[4, 2, 1, 1, 1, 2, 3, 2, 10]])
example = example.reshape(len(example), -1)
prediction = clf.predict(example)
print(prediction)

[2]

In [15]:

example = np.array([[10, 2, 1, 1, 1, 2, 3, 2, 10]])
example = example.reshape(len(example), -1)
prediction = clf.predict(example)
print(prediction)

[4]