Commenced in January 2007
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Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults

Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead


Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: Classification, falls, health risk factors, machine learning, older adults.

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[1] Health Service Executive (2018) Conditions & Treatments - Falls. Available at: (Accessed: 04/12/2018).
[2] Costa, B., Rutjes, A., Mendy, A., Freund-Heritage, R. and Vieira, E. (July 17, 2012) Can Falls Risk Prediction Tools Correctly Identify Fall-Prone Elderly Rehabilitation Inpatients? A Systematic Review and Meta-Analysis, doi:
[3] Stevenson, M., McDowell, M. and Taylor, B. (2017) Concepts for communication about risk in dementia care: A review of the literature. Doi: 10.1177/1471301216647542.
[4] Allan, LM., Ballard, CG., Rowan, EN., Kenny, RA. (2009) Incidence and Prediction of Falls in Dementia: A Prospective Study in Older People. doi: 10.1371/journal.pone.0005521.
[5] Oyebode, J. R., P. Bradley, and J. L. Allen. (2013) Relatives’ Experiences of Frontal-Variant Frontotemporal Dementia, pp. 156–166. doi: /10.1177/1049732312466294.
[6] While, C., Duane, F., Beanland, C., Koch. (2013) Medication management: The perspectives of people with dementia and family carers.
[7] Alzheimer’s Society (2007) Home from home: A report highlighting opportunities for improving standards of dementia care in care homes
[8] Godolphin, W. (2009) Shared Decision Making. Healthcare Quarterly, 12, pp. e186 - e190. doi:
[9] Thornton, H. (2003) Patients’ understanding of risk Enabling understanding must not lead to manipulation. British Medical Journal, 327, pp. 693 - 694. doi:
[10] Stevenson M & Taylor BJ (2016) Risk communication in dementia care: family perspectives. Journal of Risk Research, 18(1-2), 1-20. doi:10.1080/13669877.2016.1235604.
[11] Wang, K. (2003) Intelligent Condition Monitoring and Diagnosis Systems: A Computational Intelligence Approach. Amsterdam: IOS Press.
[12] Parodi, P. (2009) Computational intelligence techniques for general insurance, pp. 1 - 167.
[13] Brownlee, J. (March 16 2016) Understand Machine Learning Algorithms-Supervised and Unsupervised Machine Learning Algorithms. Available at:
[14] Meyer, David. (2001). Support Vector Machines The Interface to libsvm in package e1071. R News. 1.
[15] Suresh, K. and Dillibabu, R. (2018) Designing a Machine Learning Based Software Risk Assessment Model Using Naïve Bayes Algorithm.
[16] Bal, R. and Sharma, S. (May 2016) Review on Meta Classification Algorithms using WEKA.
[17] Rafiq, M., McGovern, A., Jones, S., Harris, K., Tomson, C., Gallagher, H. and de Lusignan, S. (2014) Falls in the elderly were predicted opportunistically using a decision tree and systematically using a database-driven screening tool.
[18] Zheng, B., Yoon, S.W. and Lam, S.S. (2014) Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms.
[19] Cruise, S.M., Hughes, J., Bennett, K., Kouvonen, A. and Kee, F. (2017) The impact of risk factors for coronary heart disease on related disability in older Irish adults. Doi: