Machine Learning Healthcare Data (the abstract of the paper)

In this paper, I conduct a replication study to compare the performance of different machine learning algorithms for breast cancer classification. By reproducing the statistical analysis employed in a selected study, my aim is to provide a comprehensive and reliable comparison of popular algorithms, including k-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), and XGBoost. Using a dataset comprising clinical features, I preprocess the data and evaluate the algorithms based on their classification accuracy. Additionally, I explore the training and testing times of each algorithm to gain insights into their efficiency. The comparative analysis offers valuable insights into the strengths and weaknesses of these algorithms in breast cancer classification tasks. My findings contribute to the replication and validation of the original study’s results, aiding researchers in selecting the most suitable algorithm for breast cancer diagnosis.