预测骨质疏松老年人跌倒风险的机器学习模型比较

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
German Cuaya-Simbro, A. Pérez-Sanpablo, A. Muñoz-Meléndez, Ivett Quiñones Urióstegui, Eduardo-F. Morales-Manzanares, Lidia Nuñez-Carrera
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引用次数: 3

摘要

跌倒是老年人受伤的多因素原因。骨质疏松症患者更容易跌倒。本研究的重点是研究基于时空步态参数的不同机器学习模型的性能,以预测骨质疏松症患者的跌倒。从110名骨质疏松症老年妇女(74.3±6.3岁)和143名非骨质疏松症老年妇女(68.7±6.8岁)中获得时空步态参数和前瞻性跌倒登记。我们建立了四种不同的模型,支持向量机,神经网络,决策树和动态贝叶斯网络(DBN),为每个特定的参数集使用,并比较他们的准确性,精密度,召回率和f分预测跌倒风险。F-score值表明,基于DBN的模型对跌倒风险的预测效率更高,其中以专家变量和FSMC变量、混合变量集为基础的DBN模型预测效果最好,准确率为80%,召回率为73%。结果证实了计算方法的可行性,可以补充专家的知识,预测长达12个月的时间内摔倒的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly
Abstract Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are more vulnerable to falls. The focus of this study is to investigate the performance of the different machine learning models built on spatiotemporal gait parameters to predict falls particularly in subjects with osteoporosis. Spatiotemporal gait parameters and prospective registration of falls were obtained from a sample of 110 community dwelling older women with osteoporosis (age 74.3 ± 6.3) and 143 without osteoporosis (age 68.7 ± 6.8). We built four different models, Support Vector Machines, Neuronal Networks, Decision Trees, and Dynamic Bayesian Networks (DBN), for each specific set of parameters used, and compared them considering their accuracy, precision, recall and F-score to predict fall risk. The F-score value shows that DBN based models are more efficient to predict fall risk, and the best result obtained is when we use a DBN model using the experts’ variables with FSMC’s variables, mixed variables set, obtaining an accuracy of 80%, and recall of 73%. The results confirm the feasibility of computational methods to complement experts’ knowledge to predict risk of falling within a period of time as high as 12 months.
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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