ArticleBiosensors2021
Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.Archives of gynecology and obstetrics · 2026Pooled it
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- [A Study on Spatial Autocorrelation according to the Geographical Distribution of Major Health Indicators: Focusing on Regional Units in Chungcheong Province].Jugan geon-gang gwa jilbyeong · 2024Article
- Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.ACS sensors · 2023Article
- Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk.Bioengineering (Basel, Switzerland) · 2023Article
- Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.Diagnostics (Basel, Switzerland) · 2023Article
- Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System.Sensors (Basel, Switzerland) · 2022Article
- Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch.Scientific reports · 2022Article
- Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach.Frontiers in cardiovascular medicine · 2022Article
- Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation.Gastro hep advances · 2022Article
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Authors and funding
1 author.
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Abstract
Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health-related data that can be easily measured by smartwatch users. To this end, the data corresponding to the health-related data variables provided by the smartwatch are selected from the Korea National Health and Nutrition Examination Survey. To classify the prevalence of cardiovascular disease with these selected variables, we apply logistic regression, artificial neural network, and support vector machine among machine learning classification techniques, and compare the appropriateness of the algorithm through classification performance indicators. The prediction model using support vector machine showed the highest accuracy. Next, we analyze which structures or parameters of the support vector machine contribute to increasing accuracy and derive the importance of input variables. Since it is very important to diagnose cardiovascular disease early correctly, we expect that this model will be very useful if there is a tool to predict whether cardiovascular disease develops or not.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.