Prediction of Arrhythmia with Machine Learning Algorithms
9th International Symposium on Digital Forensics and Security, ISDFS 2021, Elazığ, Türkiye, 28 - 29 Haziran 2021, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/isdfs52919.2021.9486383
- Basıldığı Şehir: Elazığ
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Arrhythmia, Bayes Theorem, Diabetes Mellitus, K-nearest Neighbors
- Maltepe Üniversitesi Adresli: Evet
Özet
The 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.