Comparison of Classification Algorithm in Predicting Stroke Disease
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2714Keywords:
Machine Learning, Logistic Regression, Random Forest, XGBoost, StrokeAbstract
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
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Copyright (c) 2022 Fenna Kemala Hutabarat, Daniel Ryan Hamonangan Sitompul, Stiven Hamonangan Sinurat, Andreas Situmorang, Ruben Ruben, Dennis Jusuf Ziegel, Evta Indra
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