<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

Higher Technological Institute of the East of State of Hidalgo

National Institute of Astrophysics, Optics and Electronics

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

Publié en ligne: 29 juin 2020

Pages: 66 - 77

Reçu: 24 sept. 2019

Accepté: 02 avr. 2020

DOI: https://doi.org/10.2478/fcds-2020-0005

Mots clés
GaitRite data, Prognosis falls, Computational methods, Osteoporosis

© 2020 German Cuaya-Simbro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

Publié en ligne: 29 juin 2020

Pages: 66 - 77

Reçu: 24 sept. 2019

Accepté: 02 avr. 2020

DOI: https://doi.org/10.2478/fcds-2020-0005

Mots clésGaitRite data, Prognosis falls, Computational methods, Osteoporosis

© 2020 German Cuaya-Simbro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
GaitRite data, Prognosis falls, Computational methods, Osteoporosis