Prediction of breast cancer based on machine learning

Abstract

Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).