Paper Title
Predictive Modeling for Breast Cancer Prediction Based on Machine Learning Classification Algorithms and Features Selection Methods
Abstract
Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast
cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this
paper, Machine Learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for
breast cancer prediction. Features selection reduce the input features by selecting only relevant features for ML model
training. Moreover, 10-fold cross-validation approach is applied to the selected features to reduce the bias of the model and
ensuring that the model is not overfitting the data. Wisconsin Diagnostic Breast Cancer (WDBC) dataset is used in order to
train and test the model. Based on the experimental results, the highest accuracy is obtained with the combination of the
LightGBM classifier and the features selected by mRMR achieving 98%.
Keywords - Breast Cancer; Classification; AI; Support Vector Machines; Logistic Regression; K-Nearest Neighbors; Naïve
Bayes; Random Forest; Light Gradient Boosting Machine; 10-Fold Cross-Validation