Machine learning algorithms for breast cancer analysis: performance and accuracy comparison

Abstract

Breast cancer, a leading cause of cancer mortality among women, necessitates early detection to improve survival rates. Traditional diagnostics face accuracy and speed limitations, prompting this study to explore machine learning for enhanced diagnostics. We applied bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), naive Bayes, support vector machine (SVM), and random forest to the Breast Cancer Wisconsin dataset, implementing a thorough methodology involving data preprocessing, feature extraction, and model validation. BERT led in accuracy at 92.5%, showcasing advanced algorithms' potential in medical diagnostics, with random forest 90.6%, SVM 89.3%, LSTM 88.7%, and naive Bayes 85.2%; also showing promising results. The study underscores the importance of incorporating machine learning, especially BERT, into clinical decision-making, potentially revolutionizing breast cancer diagnostics by improving accuracy and efficiency. We recommend healthcare practitioners integrate these algorithms into their diagnostic processes. Future research should reeefine these algorithms and extend their application to enhance patient care further.