基于遗传算法的卷积神经网络特征工程优化冠心病预测性能

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI:10.4258/hir.2024.30.3.234
Erwin Yudi Hidayat, Yani Parti Astuti, Ika Novita Dewi, Abu Salam, Moch Arief Soeleman, Zainal Arifin Hasibuan, Ahmed Sabeeh Yousif
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引用次数: 0

摘要

研究目的本研究旨在利用基于遗传算法(GA)的卷积神经网络(CNN)特征工程方法优化早期冠心病(CHD)预测。我们试图通过利用 GA 来克服传统超参数优化技术的局限性,从而在 CHD 检测中获得卓越的预测性能:方法:利用 GA 进行超参数优化,我们在复杂的组合空间中进行导航,以确定 CNN 模型的最佳配置。我们还利用信息增益进行特征选择优化,将慢性阻塞性肺病数据集转化为类似图像的 CNN 架构输入。结果显示,基于 GA 的先进 CNN 模型优于传统的优化策略:结果:基于 GA 的先进 CNN 模型优于传统方法,准确率大幅提高。优化后的模型在二元和多分类 CHD 预测任务中的准确率范围很广,在超参数优化中达到了 85% 的峰值,与机器学习算法(即奈夫贝叶斯、支持向量机、决策树、逻辑回归和随机森林)集成后的准确率为 100%:结论:将 GA 集成到 CNN 特征工程中是提高 CHD 预测准确性的有力技术。这种方法具有很高的预测可靠性,能为人工智能驱动的医疗保健领域做出重大贡献,并有可能应用于早期冠心病的临床检测。未来的工作将侧重于扩展该方法,以涵盖更广泛的冠心病数据集,并有可能与可穿戴技术相结合,用于持续健康监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.

Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies.

Results: The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.

Conclusions: The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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