Abstract
Falls are one of the leading causes of death for adults aged 65 and up. Due to the increase of falls in older adults and the severity of the resulting injuries, medical costs spent on treating affected patients are high; therefore, it is imperative to derive models that can predict falls in older adults and identify contributing factors that can be used to determine preventive methods and reduce costs. In this study, a test suite of four supervised machine learning models was created to predict falls using a fall risk dataset consisting of 593 patients and 764 internal and external features. After the training and testing phases were completed, a ten-fold cross-validation was performed to confirm the results which demonstrated accuracies of 80% and above for making predictions using the testing dataset. The results demonstrated that the model with the highest accuracy is the one that uses the support vector machine. It predicted 87% of falls correctly and had an area under the curve of 0.86. Additionally, the most significant feature in the dataset was the total number of medications which was selected along with 29 other features that were primarily prescription medications. Based on these findings, we assert that patients who take prescription medications that may result in cognitive impairment are at high risk for falls.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Berg, R.L., Cassells, J.S., Stokes, J.: Falls in older persons: risk factors and prevention. In: The Second Fifty Years: Promoting Health and Preventing Disability. National Academy Press (1992)
Bergen, G., Stevens, M.R., Burns, E.R.: Falls and fall injuries among adults aged ≥65 years — United States, 2014. MMWR Morb. Mortal. Wkly. Rep. 65(37), 993–998 (2016, in press). https://doi.org/10.15585/mmwr.mm6537a2
Burns, E., Kakara, R.: Deaths from falls among persons aged ≥65 years—United States, 2007–2016. MMWR Morb. Mortal. Wkly. Rep. 67(18), 509–514 (2018, in press). https://doi.org/10.15585/mmwr.mm6718a1
Dieleman, J.L., Baral, R., Birger, M., et al.: US spending on personal health care and public health, 1996–2013. JAMA 316(24), 2627–2646 (2016, in press). https://doi.org/10.1001/jama.2016.16885
Florence, C.S., Bergen, G., Atherly, A., Burns, E., Stevens, J., Drake, C.: Medical costs of fatal and nonfatal falls in older adults. J. Am. Geriatr. Soc. 66(4), 693–698 (2018, in press).https://doi.org/10.1111/jgs.15304
Gillain, S., et al.: Using supervised learning machine algorithm to identify future fallers based on gait patterns: a two-year longitudinal study. Exp. Gerontol. 127, 110730 (2019, in press). https://doi.org/10.1016/j.exger.2019.110730
Haddad, Y.K., Bergen, G., Florence, C.S.: Estimating the economic burden related to older adult falls by state. J. Public Health Manage. Pract. 25(2) (2019, in press). https://doi.org/10.1097/phh.0000000000000816
Hartholt, K.A., Lee, R., Burns, E.R., Beeck, E.F.V.: Mortality from falls among US adults aged 75 years or older, 2000–2016. JAMA, 321(21), 2131. (2019, in press). https://doi.org/10.1001/jama.2019.4185
Hendrich, A.L., Bender, P.S., Nyhuis, A.: Validation of the Hendrich II fall risk model: a large concurrent case/control study of hospitalized patients. Appl. Nurs. Res. 16(1), 9–21 (2003, in press). https://doi.org/10.1053/apnr.2003.yapnr2
Homer, M.L., Palmer, N.P., Fox, K.P., Armstrong, J., Mandl, K.D.: Predicting falls in people aged 65 years and older from insurance claims. Am. J. Med. 130(6) (2017 in press). https://doi.org/10.1016/j.amjmed.2017.01.003
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015, press). https://doi.org/10.1126/science.aaa8415
Kingetsu, H., Konno, T., Awai, S., Fukuda, D., Sonoda, T.: Video-based fall risk detection system for the elderly. In: 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) (2019, press). https://doi.org/10.1109/lifetech.2019.8883958
Kumar, V.: Healthcare Analytics Made Simple: Techniques in Healthcare Computing Using Machine Learning and Python. Packt Publishing, Birmingham, UK (2018)
Langarizadeh, M., Moghbeli, F.: Applying Naive Bayesian networks to disease prediction: a systematic review. Acta Inf. Med. 24(5), 364 (2016, in press). https://doi.org/10.5455/aim.2016.24.364-369
Lopez, J.M., Jacobson, L.E., Leslie, K.L., Saxe, J.M., Jensen, C.D.: Statin use predicts fall risk among older adults. Med. Sci. Sports Exerc. 49, 422 (2017, in press). https://doi.org/10.1249/01.mss.0000518036.42338.c2
Meyer, D.: Support Vector Machines: The Interface to libsvm Package e1071. FH Technikum Wien, Austria (2019)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Padrón-Monedero, A., Damián, J., Martin, M.P., Fernández-Cuenca, R.: Mortality trends for accidental falls in older people in Spain, 2000–2015. BMC Geriatr. 17(1) (2017, in press). https://doi.org/10.1186/s12877-017-0670-6
Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59. (2013, in press). https://doi.org/10.1089/big.2013.1508
Ramachandran, A., Adarsh, R., Pahwa, P., Anupama, K.R.: Machine learning-based fall detection in geriatric healthcare systems. In: 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (2018, in press). https://doi.org/10.1109/ants.2018.8710132
Serpen, G., Khan, R.H.: Real-time detection of human falls in progress: machine learning approach. Proc. Comput. Sci. 140, 238–247 (2018, in press). https://doi.org/10.1016/j.procs.2018.10.324
U.S. Census Bureau: A snapshot of the fast-growing U.S. older population (2019). https://www.census.gov/library/stories/2018/10/snapshot-fast-growing-us-older-population.html. Accessec 5 Dec 2019
Widrow, B., Lehr, M.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc. IEEE 78(9), 1415–1442 (1990). https://doi.org/10.1109/5.58323
WISQARS (Web-based Injury Statistics Query and Reporting System)|Injury Center|CDC (2019). https://www.cdc.gov/injury/wisqars/index.html. Accessed 25 Nov 2019
Zhang, Z.: Naïve Bayes classification in R. Ann. Transl. Med. 4(12), 241 (2016, in press). https://doi.org/10.21037/atm.2016.03.38
Acknowledgments
This work would not have been possible without the assistance of the medical professionals who helped gather the data. We would also like to give special thanks to Naomi Nunis who acted as a liaison to connect the School of Engineering and Computer Science representatives and the Department of Health Exercise and Sport Sciences representatives at University of the Pacific.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Her, L., Gao, J., Jacobson, L.E., Saxe, J.M., Leslie, K.L., Jensen, C. (2022). Predicting Falls in Older Adults Aged 65 and up Based on Fall Risk Dataset. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-82199-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82198-2
Online ISBN: 978-3-030-82199-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)