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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Yafooz, Wael M.S.
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Electrical & Electronics Engineering

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Klasifikasi Multikelas Infark Miokard Berdasarkan Sinyal Phonokardiogram dengan Ensemble Learning Nia Madu Marliana; Satria Mandala; Hau, Yuan Wen; Yafooz, Wael M.S.
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1121.2023

Abstract

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.
Co-Authors Nia Madu Marliana Satria Mandala Yuan Wen Hau