Classification of Health Risk Factors to Predict the Risk of Falling in Older Adults
 Health Service Executive (2018) Conditions & Treatments - Falls. Available at: https://www.hse.ie/eng/health/az/ (Accessed: 04/12/2018).
 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: doi.org/10.1371/journal.pone.0041061.
 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.
 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.
 Oyebode, J. R., P. Bradley, and J. L. Allen. (2013) Relatives’ Experiences of Frontal-Variant Frontotemporal Dementia, pp. 156–166. doi: /10.1177/1049732312466294.
 While, C., Duane, F., Beanland, C., Koch. (2013) Medication management: The perspectives of people with dementia and family carers. Doi.org/101177/1471301212444056
 Alzheimer’s Society (2007) Home from home: A report highlighting opportunities for improving standards of dementia care in care homes
 Godolphin, W. (2009) Shared Decision Making. Healthcare Quarterly, 12, pp. e186 - e190. doi: http://www.longwoods.com/content/20947.
 Thornton, H. (2003) Patients’ understanding of risk Enabling understanding must not lead to manipulation. British Medical Journal, 327, pp. 693 - 694. doi: doi.org/10.1136/bmj.327.7417.693.
 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.
 Wang, K. (2003) Intelligent Condition Monitoring and Diagnosis Systems: A Computational Intelligence Approach. Amsterdam: IOS Press.
 Parodi, P. (2009) Computational intelligence techniques for general insurance, pp. 1 - 167.
 Brownlee, J. (March 16 2016) Understand Machine Learning Algorithms-Supervised and Unsupervised Machine Learning Algorithms. Available at: https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/.
 Meyer, David. (2001). Support Vector Machines The Interface to libsvm in package e1071. R News. 1.
 Suresh, K. and Dillibabu, R. (2018) Designing a Machine Learning Based Software Risk Assessment Model Using Naïve Bayes Algorithm.
 Bal, R. and Sharma, S. (May 2016) Review on Meta Classification Algorithms using WEKA.
 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.
 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.
 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: doi.org/10.1177/0898264317726242.