A Comprehensive Comparison of Machine Learning Algorithms for Breast Cancer Prediction

The root cause of death for women is breast cancer. Early detection is essential for rectifying cancer. In this study, we explored the use of machine learning models for predicting breast cancer. We evaluated logistic regression, K-neighbors classifier, support vector machine, decision trees, random forest, and voting classifier using a dataset of breast cancer patients and clinical and demographic factors. Our results showed that the voting classifier had the highest accuracy, followed by the random forest and support vector machine. Age, family history, and breast density were essential factors influencing the development of breast cancer. These findings suggest that machine learning can be helpful in early detection and diagnosis and highlight the importance of considering these risk factors in screeningprograms.