Prediction of Breast Cancer Using Machine Learning Techniques

Breast cancer is a highly dangerous disease affecting women worldwide. Its prevalence is rapidly increasing, emphasizing the importance of early detection and preventive measures. This study focuses on utilizing various machine learning classification algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Decision Tree Classifier, to predict breast cancer in women. We will assess and compare these classifiers using metrics like accuracy, precision, recall, and f1-Score. We will utilize the breast cancer dataset from the UCI Machine Learning Repository, which is accessible to the public. The data will be split, with 80% allocated for training and 20% for testing purposes. Based on the outcomes, it is evident that the Random Forest Classifier outperformed the other classifiers across all evaluation criteria.