Abstract
It is a big challenge to assess and monitor the well-being of geriatric people due to the rise in the average population and keep track of their health status on a daily basis. Nutrition screening is a method used to find the nutrition risk in patients that can be improved by nutritional therapy. Nutrition screening strategy helps to evaluate the nutrition risk of patients which can be improved by nutritional therapy. There are different types of screening forms identified based on age group and hospital settings. The primary objective of the paper is to study the screening forms MUST, NRS-2002, and MNA to identify the correlation between the form parameters using statistical methods. The purpose of this study is to design a system to understand the nutritional status of the patient and identify patients that will be benefited from nutrition therapy. A classification algorithm of data mining is proposed to identify patients with comorbidities and hence suffering from malnutrition. It will help physicians to take the daily decision-making activities to prevent the events before occurring. The patients’ health care can be monitored by the use of devices like mobile applications by observing patients’ nutrition intake. The developed system helps to keep track of the geriatric patient’s health and improve the method of how dietician or practitioner delivers care when the patient is monitored remotely.
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Acknowledgements
We are thankful to all the study participants and all the staff from Rao Nursing Hospital and Chintamani Hospital, Pune, for helping collect data for this study.
Ethics of human subject participation Ethics committee approval for the study was obtained from the Royal Independence Ethics Committee, Pune. The informed consent form is signed by every participant to take part in the study.
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Suryawanshi, V.P., Phalnikar, R. (2021). Malnutrition Identification in Geriatric Patients Using Data Mining. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_9
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