{"title":"Optimization enabled ensemble based deep learning model for elderly falling risk prediction.","authors":"Li Chen, Wei Chen","doi":"10.1080/10255842.2025.2514802","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1520-1537"},"PeriodicalIF":1.7000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2025.2514802","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/12 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.