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Article

Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine

Department of Speech Language Pathology, School of Public Health, Honam University, 417, Eodeung-daero, Gwangsan-gu, Gwangju 62399, Korea
Int. J. Environ. Res. Public Health 2019, 16(21), 4269; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16214269
Received: 6 October 2019 / Revised: 24 October 2019 / Accepted: 31 October 2019 / Published: 3 November 2019
(This article belongs to the Special Issue Artificial Intelligence in Health Care)
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a senior welfare center. The swallowing problem associated quality of life was defined by the swallowing quality-of-life (SWAL-QOL). In order to verify the predictive power of the model, this study compared the predictive power of the Gaussian function with that of a linear algorithm, polynomial algorithm, and a sigmoid algorithm. Results: A total of 33.9% of the subjects decreased in swallowing quality-of-life. The swallowing quality-of-life prediction model for the elderly, based on the SVM, showed both preventive factors and risk factors. Risk factors were denture use, experience of using aspiration in the past one month, being economically inactive, having a mean monthly household income <2 million KRW, being an elementary school graduate or below, female, 75 years old or older, living alone, requiring time for finishing one meal on average ≤15 min or ≥40 min, having depression, stress, and cognitive impairment. Conclusions: It is necessary to monitor the high-risk group constantly in order to maintain the swallowing quality-of-life in the elderly based on the prevention and risk factors associated with the swallowing quality-of-life derived from this prediction model. View Full-Text
Keywords: swallowing quality-of-life; dysphagia; elderly living in a local community; support vector machine; risk factor swallowing quality-of-life; dysphagia; elderly living in a local community; support vector machine; risk factor
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MDPI and ACS Style

Byeon, H. Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine. Int. J. Environ. Res. Public Health 2019, 16, 4269. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16214269

AMA Style

Byeon H. Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine. International Journal of Environmental Research and Public Health. 2019; 16(21):4269. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16214269

Chicago/Turabian Style

Byeon, Haewon. 2019. "Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine" International Journal of Environmental Research and Public Health 16, no. 21: 4269. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16214269

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