ArticleJournal of clinical laboratory analysis2020
Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model.
Article in Journal of clinical laboratory analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Geospatial and machine learning analyses of cardiovascular disease mortality across the continental United States: Identifying associated variables using Shapley values.BMC public health · 2026Article
- Cardiometabolic Biomarkers and Cardiovascular Risk Stratification in Polish Military Personnel: A Chemometric Approach.International journal of molecular sciences · 2025Article
- Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.JMIR cardio · 2025Article
- Prediction of primary Hypertension in Primary Health Care Settings in Coastal Karnataka Using Artificial Neural Network.Current hypertension reviews · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Development and validation of an interpretable model for predicting sepsis mortality across care settings.Scientific reports · 2024Article
- Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.Frontiers in immunology · 2024Article
- Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.BMC complementary medicine and therapies · 2023Article
- Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.Healthcare informatics research · 2023Article
- Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine.Frontiers in medicine · 2023Article
- Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020.Frontiers in cardiovascular medicine · 2023Article
- A machine learning approach to personalized predictors of dyslipidemia: a cohort study.Frontiers in public health · 2023Article
- A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013-2017.International journal of environmental research and public health · 2022Article
- Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods.Journal of thoracic disease · 2022Article
- Comparison of conventional scoring systems to machine learning models for the prediction of major adverse cardiovascular events in patients undergoing coronary computed tomography angiography.Frontiers in cardiovascular medicine · 2022Article
- Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models.Open medicine (Warsaw, Poland) · 2022Article
- Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery.World journal of clinical cases · 2021Article
- Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells.International journal of general medicine · 2021Article
- Risk Assessment of Pulmonary Metastasis for Cervical Cancer Patients by Ensemble Learning Models: A Large Population Based Real-World Study.International journal of general medicine · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundTo establish a prediction model for cardiovascular diseases (CVD) in the general population based on random forests.
methodsA retrospective study involving 498 subjects was conducted in Xi'an Medical University between 2011 and 2018. The random forest algorithm was used to screen out the variables that greatly affected the CVD prediction and to establish a prediction model. The important variables were included in the multifactorial logistic regression analysis. The area under the curve (AUC) was compared between logistic regression model and random forest model.
resultsThe random forest model revealed the variables, including the age, body mass index (BMI), fasting blood glucose (FBG), diastolic blood pressure (DBP), triglyceride (TG), systolic blood pressure (SBP), total cholesterol (TC), waist circumference, and high-density lipoprotein-cholesterol (HDL-C), were more significant for CVD prediction; the AUC was 0.802 in CVD prediction. Multifactorial logistic regression analysis indicated that the risk factors for CVD included the age [odds ratio (OR): 1.14, 95% confidence intervals (CI): 1.10-1.17, P < .001], BMI (OR: 1.13, 95% CI: 1.06-1.20, P < .001), TG (OR: 1.11, 95% CI: 1.02-1.22, P = .023), and DBP (OR: 1.04, 95% CI: 1.02-1.06, P = .001); the AUC was 0.843 in CVD prediction. The established logistic regression prediction model was Logit P = Log[P/(1 - P)] = -11.47 + 0.13 × age + 0.12 × BMI + 0.11 × TG + 0.04 × DBP; P = 1/[1 + exp(-Logit P)]. People were prone to develop CVD at the time of P > .51.
conclusionsA prediction model for CVD is developed in the general population based on random forests, which provides a simple tool for the early prediction of CVD.
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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.