XGBoost_BiLSTM 模型的预测能力：利用电子健康数据准确检测睡眠呼吸暂停的机器学习方法

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-27 DOI:10.1007/s44196-023-00362-y

Ashir Javeed, Johan Sanmartin Berglund, A. Dallora, Muhammad Asim Saleem, P. Anderberg

引用次数: 0

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本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data

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来源期刊

International Journal of Computational Intelligence Systems 工程技术-计算机：跨学科应用

自引率

3.40%

发文量

期刊介绍： The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: -Autonomous reasoning- Bio-informatics- Cloud computing- Condition monitoring- Data science- Data mining- Data visualization- Decision support systems- Fault diagnosis- Intelligent information retrieval- Human-machine interaction and interfaces- Image processing- Internet and networks- Noise analysis- Pattern recognition- Prediction systems- Power (nuclear) safety systems- Process and system control- Real-time systems- Risk analysis and safety-related issues- Robotics- Signal and image processing- IoT and smart environments- Systems integration- System control- System modelling and optimization- Telecommunications- Time series prediction- Warning systems- Virtual reality- Web intelligence- Deep learning