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

Author

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  • Haewon Byeon

    (Department of Speech Language Pathology, School of Public Health, Honam University, 417, Eodeung-daero, Gwangsan-gu, Gwangju 62399, Korea)

Abstract

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.

Suggested Citation

  • Haewon Byeon, 2019. "Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine," IJERPH, MDPI, vol. 16(21), pages 1-9, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4269-:d:283166
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    Citations

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    Cited by:

    1. Haewon Byeon, 2020. "Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?," IJERPH, MDPI, vol. 17(7), pages 1-14, April.
    2. Haewon Byeon, 2021. "Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    3. Akiko Shimizu & Ryuichi Ohta & Hana Otani & Chiaki Sano, 2021. "The Contribution of Temporal Flat Lateral Position on the Mortality and Discharge Rates of Older Patients with Severe Dysphagia," IJERPH, MDPI, vol. 18(16), pages 1-9, August.
    4. Amanda Björnwall & Ylva Mattsson Sydner & Afsaneh Koochek & Nicklas Neuman, 2021. "Eating Alone or Together among Community-Living Older People—A Scoping Review," IJERPH, MDPI, vol. 18(7), pages 1-42, March.
    5. Moon-Young Chang & Gihyoun Lee & Young-Jin Jung & Ji-Su Park, 2020. "Effect of Neuromuscular Electrical Stimulation on Masseter Muscle Thickness and Maximal Bite Force among Healthy Community-Dwelling Persons Aged 65 Years and Older: A Randomized, Double Blind, Placebo," IJERPH, MDPI, vol. 17(11), pages 1-10, May.

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