15037046024/breast-cancer-diagnosis

Breast cancer diagnosis using multiple statistical/machine learning techniques: XGBoost, support vector machine, random forest, k-neighbours, and deep learning

Breast cancer diagnosis using statistical techniques in Python (Scikit-learn, XGBoost, Keras)

• includes application of grid search, SMOTE sampling, and visualization using principal component analysis
• data used is from UCI Machine Learning Repository

For most models, k-fold cross validation was performed with grid search to find optimal model parameters.

• read about k-fold cross validation at https://towardsdatascience.com/cross-validation-explained-evaluating-estimator-performance-e51e5430ff85 and https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html
• read about grid search at https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html and https://towardsdatascience.com/using-3d-visualizations-to-tune-hyperparameters-of-ml-models-with-python-ba2885eab2e9

XGBoost

XGBoost, or extreme gradient boosting, is a top machine learning model and the dominating algorithm among competitions.

• supports many tunings parameters such as learning rate and early stopping
• open source code and download at https://pypi.org/project/xgboost/
• paper available at https://arxiv.org/abs/1603.02754 by Tianqi Chen
• as a tree boosting algorithm, minimal data-preprocessing is needed (i.e. data normalization/scaling)

Test accuracy achieved with XGBoost

Example tree

Feature importance

Training/validation error

Training/validation loss

Random Forest Classifier

• RFC utilizes multiple decision trees and averages between them to make predictions
• documentation to sklearn's RFC at https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
• again, as a tree based model, RFC requires minimal data preprocessing

Test accuracy achieved with RFC

Example tree

Support Vector Machine

• note PCA was used to bring dimensionality down to 2 in order to plot the hyperplane
• support vector machines classifies models by finding a hyperplane to separate classes while maximizing the margin distance from the classes
• as a distance based method, SVM requires data normalization to ensure no features take precendence over others
• documentation at https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC

Accuracy achieved with SVM

Linear decision boundary

Radial basis function decision boundary

K-Neighbours

• again, PCA was used to bring dimensionality down to 2 for plotting purposes
• documentation at https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

Accuracy achieved with KNC

Nearest neighbours classification plot

Deep Learning

• simple deep learning model used with residual connections similar to Resnet v1 to ensure "deep" networks train, at a minimum, as well as "shallow" networks
• the goal of deep learning is to calculate weights and biases within the network using gradients and error functions
• documentation at https://keras.io/

Model

Oversampled results

Non-oversampled results