Breast Cancer Prediction and Diagnosis: A Comparative Study Analysis Using Machine Learning Algorithms

The motive of this article is to discover the algorithms for machine learning for the detection and diagnosis of breast cancer. Through rigorous analysis and comparison, we aim to identify the most effective algorithm in predicting and diagnosing breast cancer. Notably, with an exceptional accuracy of 97.2%, our results show that Support Vector Machine is the best performer. Using Scikit-learn toolkit, in conjunction with the Google Colab and Anaconda environment, this study highlights the critical role that machine learning plays in enhancing cancer management approaches. Apart from attaining remarkable precision levels, the application of these algorithms for cancer prognosis and diagnosis has other noteworthy benefits. This customized approach improves treatment results and problems while lowering the likelihood of adverse effects. By using machine learning approach, this research focus on improving the care for breast cancer data and open the door to a more aggressive and successful strategy for battling this common and deadly illness.