Analysis of Various Machine Learning Classifiers for Prediction and Classification of Breast Cancer

Everyone is impacted by cancer, the most common disease in the world. The classification of cancerous cell types is one utilization of intelligent retrieval in medical science. To increase the survival rate, it is essential to identify breast cancer early. To accurately detect and anticipate breast cancer in its early stages, a sophisticated framework is therefore required. Using machine learning classification techniques, the article evaluates the model's prediction power. The accuracy, recall, support, fl-score and AUC-ROC curve metrics were used to assess the classification performance of the Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), LR, K-Nearest Neighbors (KNN), Bagging, and Boosting Classifiers. According to the results, Support Vector (SVM), Bagging and Adaboost classifiers, Random Forest were successful in achieving the best accuracy of 96.00%.