Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Mohamed Ebrahim (), Ahmed Ahmed Hesham Sedky and Saleh Mesbah
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Mohamed Ebrahim: Department of Information Systems, College of Computing and Information Technology, Arab Academy for Science Technology and Maritime Transport, Alexandria 511511, Egypt
Ahmed Ahmed Hesham Sedky: Department of Information Systems, College of Computing and Information Technology, Arab Academy for Science Technology and Maritime Transport, Alexandria 511511, Egypt
Saleh Mesbah: Department of Information Systems, College of Computing and Information Technology, Arab Academy for Science Technology and Maritime Transport, Alexandria 511511, Egypt

Data, 2023, vol. 8, issue 2, 1-12

Abstract: Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy.

Keywords: deep learning; feature selection; machine learning; breast cancer prediction; tumor classification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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