Optimization enabled ensemble based deep learning model for elderly falling risk prediction.

IF 1.7 4区 医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Li Chen, Wei Chen
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引用次数: 0

Abstract

Predicting fall risk in the elderly is crucial for enhancing safety and well-being. Aging and chronic diseases often impair balance, increasing fall risk. This study aims to develop an advanced fall risk prediction model using an optimized deep learning approach. Data undergoes pre-processing and augmentation to increase size, then is fed into an ensemble learning model,like Extreme Gradient Boosting (XGBoost), One Dimensional Convolutional Neural Network, and Deep Belief Network. The model is trained with a novel Double Exponential Lyrebird Optimization algorithm, combining double exponential smoothing and Lyrebird Optimization . Experimental results show that DELOA-based ensemble learning model achieved better results.

基于集成的深度学习模型优化老年人跌倒风险预测。
预测老年人跌倒风险对提高安全和福祉至关重要。衰老和慢性疾病经常损害平衡,增加跌倒的风险。本研究旨在利用优化的深度学习方法开发一种先进的跌倒风险预测模型。数据经过预处理和增强以增加大小,然后被输入到一个集成学习模型中,如极端梯度增强(XGBoost)、一维卷积神经网络和深度信念网络。采用双指数平滑和Lyrebird优化相结合的双指数Lyrebird优化算法对模型进行训练。实验结果表明,基于deloy的集成学习模型取得了较好的学习效果。
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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
179
审稿时长
4-8 weeks
期刊介绍: The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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