In Annals of medicine and surgery (2012)
Background : Breast cancer disease is the most common cancer in US women and the second cause of cancer death among women.
Objectives : To compare and evaluate the performance and accuracy of the key supervised and semi-supervised machine learning algorithms for breast cancer prediction.
Materials and methods : We have used nine machine learning classification algorithms for supervised (SL) and semi-supervised learning (SSL): 1) Logistic regression; 2) Gaussian Naive Bayes; 3) Linear Support vector machine; 4) RBF Support vector machine; 5) Decision Tree; 6) Random Forest; 7) Xgboost; 8) Gradient Boosting; 9) KNN. The Wisconsin Diagnosis Cancer dataset was used to train and test these models. To ensure the robustness of the model, we have applied K-fold cross-validation and optimized hyperparameters. We have evaluated and compared the models using accuracy, precision, recall, F1-score, and ROC curves.
Results : The results of all models are inspiring using both SL and SSL. The SSL has high accuracy (90%-98%) with just half of the training data. The KNN model for the SL and logistic regression for the SSL achieved the highest accuracy of 98.
Conclusion : The accuracies of SSL algorithms are very close to the SL algorithms. The accuracies of all models are in the range of 91-98%. SSL is a promising and competitive approach to solve the problem. Using a small sample of labeled and low computational power, the SSL is fully capable of replacing SL algorithms in diagnosing tumor type.
Al-Azzam Nosayba, Shatnawi Ibrahem
ANN, Artificial Neural Network, Breast cancer, Cov, covariance, Diagnosis, EDA, Exploratory Data Analysis, FNA, fine needle aspirate, FPR, False positive rate, ID3, Information Gain, KNN, K- nearest neighbor, MRI, Magnetic resonance imaging, Machine learning algorithms, RBF, Radial Basis Function, ROC, Receiver Operator Characteristic, SL, Supervised Learning, SSL, Semi-Supervisd Learning, SVM, Support vector machine, Semi-supervised, Supervised, TPR, True positive rate, WDBC, Wisconsin Diagnostic Breast Cancer, Xgboost, eXtreme Gradient Boosting, t-SNE, t-distributed Stochastic Neighbor Embedding