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Download PDFOpen PDF in browserAn Efficient Machine Learning Algorithm for Breast Cancer PredictionEasyChair Preprint 870615 pages•Date: August 25, 2022AbstractCancer is a leading cause of death worldwide, with breast cancer (BC) being the most common and prevalent with 2.26 million cases each year, and the main cause of women’s deaths, so early and correct detection to discover BC in its first phases, help to avoid death by describing the appropriate treatment and to maintain human life. Cancer cells are divided into two types Malignant and Benign. The first type is more dangerous and the second type is less dangerous. Due to the existence of artificial intelligence (AI) and the great direction to the use of machine learning in medicine, doctors get accurate results for diagnosis. In this paper, we tend to use the Wisconsin Breast Cancer Patients Database (WBCD) which has been collected from the UCI repository. In this paper, the WBCD dataset is divided into 75% for training and 25% for testing using a split test train. We addressed to research the performance of various well-known algorithms in the discovery of BC such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Artificial Neural Networks (ANN). High results indicate that the RF algorithm is 98.2% superior to the rest of the machine learning algorithms Keyphrases: WBCD, breast cancer, classification algorithms, machine learning Download PDFOpen PDF in browser |
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