Applying Different Machine Learning Models to Predict Breast Cancer Risk
نویسنده
چکیده
In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of all models evaluated against various number of features, and examine the reasons behind their varying performances. Keywords—Breast cancer, Feature selection, Machine learning, Binary classification, SVM, Logistic regression, Random forest, Naive Bayes
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