Klasifikasi Denyut Jantung dari Rekaman Eletrokardiogram(EKG) Menggunakan Metode Random Forest
AbstractHeart disease is a very deadly disease, to reduce heart disease can be detected with a heart recording device (ECG), ECG is used to measure the electrical activity of the heart and is usually used in medicine to detect heart disease because of its simplicity and non-invasive nature. By analyzing the electrical signals at each heartbeat and the combination of impulse waveforms created by the various specialized tissues of the heart. By using the RR-Interval feature, you can detect heart disease by measuring the distance between the RRs of the heart recording signal. The dataset used is the MIT-BIH dataset, resulting in 5 main classes: non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q). Furthermore, the class results are classified using the Random Forest method. Random Forest is a classification algorithm with a good level of accuracy. Random Forest is an ensemble method consisting of several decision trees as a classifier. From the tests carried out using the confusion matrix method, the accuracy of the classification using Random Forest is 85%.
Copyright info for authors
1. Authors hold the copyright in any process, procedure, or article described in the work and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors retain publishing rights to re-use all or portion of the work in different work but can not granting third-party requests for reprinting and republishing the work.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.