使用基于基因组数据的 CNN 识别心房颤动

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Jaehyung Lee, Oh-Seok Kwon, Gayeon Ryu, Hangsik Shin, Hui-Nam Pak
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引用次数: 0

摘要

心房颤动(房颤)是最常见的心律失常,也是 21 世纪心血管疾病的主要流行病。由于心房颤动常常在没有症状的情况下发展,因此早期诊断和干预至关重要。这项研究旨在利用全基因组关联研究和卷积神经网络(CNN)来识别房颤。来自 6358 个个体的基因组数据被用于开发 CNN 模型,并应用 L2 正则化防止过度拟合。在不同的 p 值阈值下,L2-正则化 CNN 的表现明显优于常规 CNN。例如,在 p < 0.0001 时,L2-正则化 CNN 的准确率为 0.731 ± 0.071,而常规 CNN 的准确率为 0.703 ± 0.055。当 p < 0.001 时,L2-正则化 CNN 的准确率为 0.630 ± 0.089,而普通 CNN 的准确率为 0.577 ± 0.095。这表明 L2 正则化显著提高了模型性能。虽然常规 CNN 在某些情况下表现出更高的准确率,例如在 p < 0.01 时达到 0.984 ± 0.015,而 L2 正则化 CNN 为 0.970 ± 0.020,但随着 p 值阈值变得越来越严格,模型之间的性能差异也在缩小。总体而言,L2 正则化不仅提高了模型的性能和稳定性,还缩小了在更严格的 p 值条件下模型之间的性能差距。这些研究结果突出表明,L2 正则化的 CNN 可以显著提高基因组研究的性能,为 AF 识别研究提供了一种比传统多基因风险评分方法更有效的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Atrial Fibrillation Identification Using CNNs Based on Genomic Data

Atrial Fibrillation Identification Using CNNs Based on Genomic Data

Atrial fibrillation (AF) is the most common cardiac arrhythmia and a major cardiovascular disease epidemic of the 21st century. Early diagnosis and intervention are crucial as AF often progresses without symptoms. This study aims to identify AF using genome-wide association studies and convolutional neural networks (CNN). Genomic data from 6,358 individuals were used to develop a CNN model, with L2 regularization applied to prevent overfitting. The L2-regularized CNN significantly outperformed the regular CNN across various p-value thresholds. For instance, at p < 0.0001, the L2-regularized CNN achieved an accuracy of 0.731 ± 0.071 compared to 0.703 ± 0.055 for the regular CNN. At p < 0.001, the L2-regularized CNN showed an accuracy of 0.630 ± 0.089, while the regular CNN had 0.577 ± 0.095. This demonstrates a notable improvement in model performance with L2 regularization. Although the regular CNN showed higher accuracy in some scenarios, such as achieving 0.984 ± 0.015 at p < 0.01 compared to 0.970 ± 0.020 for the L2-regularized CNN, the performance difference between the models decreased as the p-value threshold became more stringent. Overall, L2 regularization not only improved the model’s performance and stability but also reduced the performance gap between the models under stricter p-value conditions. These findings highlight that L2-regularized CNNs can significantly enhance performance in genomic studies, offering a more effective alternative to traditional polygenic risk score methods for AF identification study.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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