Comparison of Classification Algorithm in Predicting Stroke Disease - Hutabarat, Fenna Kemala; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2022)
ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Stroke