Cardiac Diagnosis System for Heart Disease Patients using Machine Learning Algorithms

• Author(s): MOE THIDA
• Paper ID: 1702381
• Page: 161-165
• Published Date: 24-06-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 12 June-2020

Abstract

Nowadays, machine learning techniques applied to medical image analysis have become an essential role according to many advances in digital imaging. Analyzing ECG medical images to diagnose types of arrhythmias which may cause sudden cardiac death is very demanding in healthcare sector. In this paper, J48 Decision Tree and Support Vector Machine (SVM) are utilized for cardiac diagnosis classification system. And, ECG medical images are applied to classify four different arrhythmia types and one normal condition type in this work. The proposed system is experimented using benchmark ECG images from MIT-BIH arrhythmia database. The diagnosis performance of the system is shown by using classification accuracy, sensitivity and specificity analysis. According to the experimental results, the proposed system achieves the satisfying classification accuracy over 80 percent (%).

Keywords

Arrhythmias, Decision Tree, ECG, Support Vector Machine, MIT-BIH.

Citations

IRE Journals:
MOE THIDA "Cardiac Diagnosis System for Heart Disease Patients using Machine Learning Algorithms" Iconic Research And Engineering Journals Volume 3 Issue 12 2020 Page 161-165

IEEE:
MOE THIDA "Cardiac Diagnosis System for Heart Disease Patients using Machine Learning Algorithms" Iconic Research And Engineering Journals, 3(12)