Breast Cancer Forecasting Using Machine Learning Algorithms

Abstract

Some of the most prevalent and significant causes for malignancies in women is breast cancer. It is presently a widespread health problem, and it has recently become more frequent. The greatest method for managing breast cancer symptoms is early identification. The only kind of cancer that primarily affects women globally is breast cancer, which has the potential to be a major cause of mortality. Early detection of breast cancer is crucial in order to properly treat it and save many lives. This paper covers the results and analyses of several machine learning algorithms for identifying breast cancer. Several machine learning models used the information once it was analyzed. In this paper the Random forests and SVC algorithms were applied and compare the performance of these algorithms. The dataset was taken from UCI repository. Analyze and compare the classifiers' performance in terms of accuracy, precision, and f1-Score in addition. For implementing the ML algorithms, the dataset was split among training and testing phases. The notebook application Jupyter was used to implement these models. When compared to the other two models, it was successfully proven that the SVC model offers the best results. SVC's accuracy of 93% is greater than the method described earlier in that regard. The method used by this model, which will classify cancer into benign and malignant categories, yields the best results.