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Sri Kusuma Dewi
Islamic University of Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Decision Support System for Heart Disease Diagnosing Using K-NN Algorithm Tito Yuwono; Noor Akhmad Setiawan; Adi Nugroho; Anugrah Galang Persada; Ipin Prasojo; Sri Kusuma Dewi; Ridho Rahmadi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.661 KB) | DOI: 10.11591/eecsi.v2.776

Abstract

Heart disease is a notoriously dangerous disease whichpossibly causing the death. An electrocardiogram (ECG) is used fora diagnosis of the disease. It is often, however, a fault diagnosis by adoctor misleads to inappropriate treatment, which increases a riskof death. This present work implements k-nearest neighbor (K-NN)on ECG data to get a better interpretation which expected to help adecision making in the diagnosis. For experiment, we use an ECGdata from MIT BIH and zoom in on classification of three classes;normal, myocardial infarction and others. We use a single decisionthreshold to evaluate the validity of the experiment. The resultshows an accuracy up to 87% with a value of K = 4
Co-Authors Adi Nugroho Anugrah Galang Persada Ipin Prasojo Noor Akhmad Setiawan Ridho Rahmadi Tito Yuwono, Tito