Gürsoy, GüneşVarol, Asaf2024-07-122024-07-122021978-1-6654-4481-110.1109/ISDFS52919.2021.94863832-s2.0-85114698521https://doi.org/10.1109/ISDFS52919.2021.9486383https://hdl.handle.net/20.500.12415/73959th International Symposium on Digital Forensics and Security (ISDFS) -- JUN 28-29, 2021 -- Fırat Univ, Elazig, TURKEYThe present study uses the age, sex, diabetes mellitus, and arrhythmia data of patients from the datasets presented in an existing study to predict arrhythmia with machine learning algorithms, K-Nearest Neighbors (KNN), and Naive Bayes methods. The outputs are schematically presented, and the conclusions related to the Bayes theorem and KNN algorithms are compared. In the case of increasing the value of neighboring k in the KNN method, it is seen that the accuracy rate approaches the result obtained from the Naive Bayes method.eninfo:eu-repo/semantics/closedAccessArrhythmiaDiabetes MellitusBayes TheoremK-Nearest NeighborsConference ObjectN/AWOS:000844418700040N/A