scholarly journals Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey

Biosensors ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 228
Author(s):  
Min-Jeong Kim
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Support Vector Machine ◽  
Prediction Model ◽  
National Health ◽  
Support Vector ◽  
Nutrition Examination Survey ◽  
Related Data ◽  
Health And Nutrition ◽  
Health Related

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.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

Development of the Hypertension Index Model in General Adult Using the Korea National Health and Nutritional Examination Survey and the Korean Genome and Epidemiology Study (Preprint)

2021 ◽  
Author(s):  
Myung Jae Seo ◽  
Sung Gyun Ahn ◽  
Yong-Jae Lee ◽  
Jong Koo Kim
Keyword(s):  
Prediction Model ◽  
National Health ◽  
Prediction Models ◽  
Classification Model ◽  
Nutrition Examination Survey ◽  
Epidemiology Study ◽  
Index Model ◽  
Health And Nutrition ◽  
Specific Prediction ◽  
And Control

BACKGROUND Hypertension, a risk factor for cardiovascular disease and all-cause mortality, has been increasing. Along with emphasizing awareness and control of hypertension, predicting the incidence of hypertension is important. Several studies have previously reported prediction models of hypertension. However, among the previous models for predicting hypertension, few models reflect various risk factors for hypertension. OBJECTIVE We constructed a sex-specific prediction model using Korean datasets, which included socioeconomic status, medical history, lifestyle-related variables, anthropometric status, and laboratory indices. METHODS We utilized the data from the Korea National Health and Nutrition Examination Survey from 2011 to 2015 to derive a hypertension prediction model. Participants aged 40 years or older. We constructed a sex-specific hypertension classification model using logistic regression and features obtained by literature review and statistical analysis. RESULTS We constructed a sex-specific hypertension classification model including approximately 20 variables. We estimated its performance using the Korea National Health and Nutrition Examination Survey dataset from 2016 to 2018 (AUC = 0.807 in men, AUC = 0.854 in women). The performance of our hypertension model was considered significant based on the cumulative incidence calculated from a longitudinal dataset, the Korean Genome and Epidemiology Study dataset. CONCLUSIONS We developed this hypertension prediction model using features that could be collected in a clinical office without difficulty. Individualized results may alert a person at high risk to modify unhealthy lifestyles.

Abstract 15214: Food Insecurity and Cardiovascular Disease in NHANES (National Health and Nutrition Examination Survey) 2011-2016

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
MONICA M DELSON ◽  
Janice F Bell ◽  
Tequila S Porter ◽  
Julie T Bidwell
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Food Insecurity ◽  
National Health ◽  
The United States ◽  
Nutrition Examination Survey ◽  
Health And Nutrition ◽  
Artery Disease

Background: Adherence to a heart-healthy diet is foundational for the prevention, management, and treatment of cardiovascular disease (CVD). Despite the fact that adhering to dietary guidelines may be challenging in the context of food insecurity, little is known about the likelihood of food insecurity in persons with CVD. Hypothesis: We hypothesized that persons with CVD (hypertension, coronary artery disease, heart failure, or stroke) would have significantly higher odds of food insecurity. Methods: This was an analysis of data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative, cross-sectional study of health in the United States. All adults aged 19 years or older with food insecurity data were included across 3 cycles of NHANES (2011-2016). Food insecurity was measured using the 10-item Food Security Scale. CVD diagnosis was measured by self-report. Risk for food insecurity by CVD diagnosis was examined using multivariable logistic regression models, incorporating NHANES sample and person weights, and controlling for common sociodemographic confounders (age, gender, race/ethnicity, education, marital status). Results: The sample consisted of 17,175 persons (weighted study N =229,247,659). Slightly more than half were male (51.9%), and most were non-Hispanic white (65.1%). Just under half (45.6%) were in early adulthood (19-44 years), 35.3% were in middle adulthood (45-64 years), and 18.6% were in late adulthood (≥65 years). One quarter (25.9%) were food insecure. Consistent with our hypothesis, diagnosis of any CVD (stroke, heart failure, coronary artery disease, or hypertension) was significantly associated with higher likelihood for food insecurity (stroke: OR=2.18; 95% CI 1.83-2.60; p<0.001; heart failure OR=1.94, 95% CI 1.46-2.57, p<0.001; coronary artery disease: OR=1.90, 95% CI 1.49-2.43, p<0.001; and hypertension: OR=1.25, 95% CI 1.10-1.42, p=0.001). Conclusions: Diagnoses of hypertension, stroke, coronary artery disease, and heart failure were all significantly associated with higher risk for food insecurity. Given the necessity of dietary modification in CVD, further efforts to study food insecurity in CVD alongside other social determinants of health are urgently needed.

Sign in / Sign up

Export Citation Format

Share Document