ArticleBMC cardiovascular disorders2021
Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.
Article in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Predictors of childhood vaccination and recent influenza vaccination in older Brazilian adults: an analysis using conventional regression and machine learning approaches.Frontiers in public health · 2026Article
- The interpretable machine learning model for depression associated with heavy metals via EMR mining method.Scientific reports · 2025Article
- Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.Frontiers in public health · 2025Article
- Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2024Article
- The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
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7 authors at 1 institution in 1 country.
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Abstract
backgroundAnnual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination
methodsAdults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups.
resultsThe accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%).
conclusionsThe machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.
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