ArticlePloS one2019
Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Application of Artificial Intelligence and Machine Learning in Left Ventricular Assist Device Implantation: A Systematic Review.Artificial organs · 2025Pooled 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
- Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization.Journal of medical systems · 2019Pooled it
- Early prediction of 3-month functional outcome after acute ischemic stroke: an explainable model based on routine clinical data.Frontiers in neurology · 2026Article
- Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.Communications medicine · 2025Article
- Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan.BMC public health · 2025Article
- Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.PLOS digital health · 2025Article
- Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.BMC public health · 2025Article
- Machine learning prediction of depression in culturally diverse families: Findings from the Korea Community Health Survey.Frontiers in public health · 2025Article
- Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.PloS one · 2025Article
- Development and validation of a machine learning predictive model for perioperative myocardial injury in cardiac surgery with cardiopulmonary bypass.Journal of cardiothoracic surgery · 2024Article
- Article
- Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks.Frontiers in neurology · 2023Article
- Article
- Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19.Infectious Disease Modelling · 2022Article
- Adult Spinal Deformity Surgery and Frailty: A Systematic Review.Global spine journal · 2022Article
- Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence.Frontiers in cardiovascular medicine · 2022Article
- Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.Computational intelligence and neuroscience · 2022Article
- Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.International journal of environmental research and public health · 2021Article
- Prediction of Atrial Fibrillation Recurrence after Thoracoscopic Surgical Ablation Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsNon-linear models by machine learning may identify different risk factors with different weighting in comparison to conventional linear models. METHODS AND
resultsThe analyses were performed in 15,933 patients included in the Shinken Database (SD) 2004-2014 (n = 22,022) for whom baseline data of blood sampling and ultrasound cardiogram and follow-up data at 2 years were available. Using non-linear models with machine learning software, 118 risk factors and their weighting of risk for all-cause mortality, heart failure (HF), acute coronary syndrome (ACS), ischemic stroke (IS), and intracranial hemorrhage (ICH) were identified, where the top two risk factors were albumin/hemoglobin, left ventricular ejection fraction/history of HF, history of ACS/anti-platelet use, history of IS/deceleration time, and history of ICH/warfarin use. The areas under the curve of the developed models for each event were 0.900, 0.912, 0.879, 0.758, and 0.753, respectively.
conclusionHere, we described our experience with the development of models for predicting cardiovascular prognosis by machine learning. Machine learning could identify risk predicting models with good predictive capability and good discrimination of the risk impact.
Indexed as
Identifiers
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.