raaaouf / decision-tree-for-breast-cancer-detection

Breast Cancer Detection with Decision trees Algorithm And Bagging Normalizing

Decision-Tree-for-Breast-cancer-detection

implemening decision trees algorithm with bagging as a normlizing technic to predict breast cancer from routine blood tests

DATA

The dataset contains only the following ten attributes:

• Age: age of the patient (years)
• BMI: body mass index (kg/m²)
• Glucose: glucose concentration in blood (mg/dL)
• Insulin: insulin concentration in blood (microU/mL)
• HOMA: Homeostatic Model Assessment of Insulin Resistance (glucose multiplied by insulin)
• Leptin: concentration of leptin — the hormone of energy expenditure (ng/mL)
• Adiponectin: concentration of adiponectin — a protein regulating glucose levels (micro g/mL)
• Resistin: concentration of resistin — a protein secreted by adipose tissue (ng/mL)
• MCP.1: concentration of MCP-1 — a protein that recruits monocytes to the sites of inflammation due to tissue injury or inflammation (pg/dL)
• Classification: Healthy controls (1) or patient (2)

Data Visualisation

As you can see there are two classes with almost the same number of samples

Results with bagging

from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier()

bagging_clf.fit(X_train, y_train.ravel())
y_pred_bag = bagging_clf.predict(X_test)

bag_cm = confusion_matrix(y_test, y_pred_bag)

plot_confusion_matrix(bag_cm, [0, 1])
plt.show()

After ploting the confusion matrix we can see our model classified correctly all instances in the test set using bagging normalization.

License