DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-9 ( Sep, 2023 ) | |||||||||
Paper Title |
Predictive Modeling for Breast Cancer Prediction Based on Machine Learning Classification Algorithms and Features Selection Methods | |||||||||
Author Name |
Arar Altawil, Laiali Almazaydeh, Reyad Alsalameen, Bilal Alqudah, Khaled Elleithy | |||||||||
Affilition |
1Abdul Aziz Al Ghurair School of Advanced Computing, Luminus Technical University College, Amman, Jordan 2,3Department of Software Engineering, Al-Hussein Bin Talal University, Ma’an, 71111, Jordan 4Department of Computer Engineering, Al-Hussein Bin Talal University, Ma’an, 71111, Jordan 5Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, 06604, USA | |||||||||
Pages |
25-31 | |||||||||
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 | |||||||||
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