The aging world population is a challenge for global healthcare. Caring for the elderly is particularly stressful and difficult for informal caregivers. Accordingly, automated systems to assist informal caregivers will fill a great need. In this paper, a system prototype is developed to provide recommendations for elderly care to informal caregivers applying case-based reasoning (CBR) techniques. The recommendations are divided into four areas, namely, physical therapy, food, emotional, and exercise regimes. Real data was used as the input to create a case database and identify weighted attributes in consultation with experts. A novelty in our application of CBR is the use of tailored similarity measures depending on the attribute type. The system prototype performs with high accuracy.
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Wongpun et al. (2017) studied this question.