ArticleInternational journal of environmental research and public health2021
Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Predicting and classifying type 2 diabetes using a transparent ensemble model combining random forest, k-nearest neighbor, and neural networks.Scientific reports · 2025Article
- Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions.Nutrients · 2025Review
- Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients.Nutrients · 2025Article
- Cross-sectional study on exercise capacity in obese patients with severe obstructive sleep apnea syndrome.Frontiers in physiology · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- The role of diet in cancer: the potential of shaping public policy and clinical outcomes in the UK.Genes & nutrition · 2024Review
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.BMC public health · 2024Article
- Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.Bioengineering (Basel, Switzerland) · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Article
- An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data.Frontiers in physiology · 2024Article
- Characteristics of cardiopulmonary exercise capacity in adults with different degrees of obesity.Frontiers in physiology · 2024Article
- Emerging Perspectives on the Set of Conditions That Lead to the Emergence of Metabolic Syndrome.Journal of personalized medicine · 2023Review
- Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.Biomimetics (Basel, Switzerland) · 2023Article
- Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.Scientific reports · 2023Article
- Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.Diagnostics (Basel, Switzerland) · 2023Article
- A machine learning approach to personalized predictors of dyslipidemia: a cohort study.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4-7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (
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What OpenQuestion holds
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.