利用集合学习对基于声心动图信号的心肌梗死进行多类分类

Nia Madu Marliana, Satria Mandala, Yuan Wen, Hau, Wael M.S. Yafooz
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引用次数: 0

摘要

心肌梗死(MI)是一种严重的心血管疾病,在全世界的死亡率都很高。早期检测和持续治疗可大大降低心血管疾病的死亡率。然而,目前需要一种高效的模型,能够在不依赖训练有素的临床专家的情况下实现心脏病的早期检测。使用声心动图(PCG)信号并实施集合学习模型的心肌梗死研究仍然相对较少,往往导致准确率和检出率较低。本研究旨在采用集合学习模型,利用 PCG 信号将心肌梗死分为不同类别。在现阶段的研究中,包括随机森林和逻辑回归在内的几种分类算法利用从音频信号中提取的特征作为集合学习的基本模型。对模型性能的评估显示,堆叠模型的准确率达到 96%。这些结果表明,我们的系统可以适当、准确地对 PCG 数据中的 MI 进行分类。我们相信,这项研究的结果将提高心脏病发作的诊断和治疗水平,使其更加有效和准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning
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.
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