Analysis of ML-Based Classifiers for the Prediction of Breast Cancer

Breast cancer is a significant health issue affecting women globally. Early detection and diagnosis are crucial for effective treatment and improved survival rates. Machine learning (ML)-based classifiers have gained popularity in breast cancer prediction due to their ability to analyze large datasets and identify complex patterns. This study presents an analysis of ML-based classifiers for breast cancer prediction using the UCI Wisconsin breast cancer dataset. Extra Tree Classifier-based feature selection is used to identify the most important features for classification. The classifiers used in this study include Logistics Regression, SVM, kNN, Random Forest, Naive Bayes, and Decision Tree. The performance of each classifier is evaluated based on various metrics, including accuracy, sensitivity, and F1 score. The results show that SVM and Logistics Regression provide the highest performance. This study highlights the importance of feature selection in improving the performance of ML-based classifiers for breast cancer prediction. The findings of this study can contribute to the development of accurate and efficient breast cancer prediction models, which can aid in early detection and timely treatment, ultimately improving patient outcomes.