ArticleScientific reports2022
Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.Archives of gynecology and obstetrics · 2026Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- A hybrid deep learning and large language model framework for MACE risk prediction and evidence-based clinical recommendations.Frontiers in cardiovascular medicine · 2026Article
- Cardiovascular risk prediction via ensemble machine learning and oversampling methods.Scientific reports · 2025Article
- Predicting Major Adverse Cardiovascular Events After Cardiac Surgery Using Combined Clinical, Laboratory, and Echocardiographic Parameters: A Machine Learning Approach.Medicina (Kaunas, Lithuania) · 2025Article
- Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events.American journal of preventive cardiology · 2025Article
- OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Personalised Risk Modelling for Older Adult Cancer Survivors: Combining Wearable Data and Self-Reported Measures to Address Time-Varying Risks.Sensors (Basel, Switzerland) · 2025Article
- Prediction of cardiovascular risk using machine-learning methods. Sex-specific differences.Frontiers in cardiovascular medicine · 2025Article
- Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.Cureus · 2024Review
- Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact.BioData mining · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery.The Journal of thoracic and cardiovascular surgery · 2023Article
- Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.Sensors (Basel, Switzerland) · 2023Review
- Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Smart Consumer Wearables as Digital Diagnostic Tools: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score compared against was the Framingham Risk Score (FRS). The outcome variables were low or high risk based on calcium score 0 or calcium score 100 and above. Ensemble MLAs were built based on naive bayes, random forest and support vector classifier for low risk and generalized linear regression, support vector regressor and stochastic gradient descent regressor for high risk categories. MLAs were trained on 600 Southeast Asians aged 21 to 69 years free of cardiovascular disease. All MLAs outperformed the FRS for low and high-risk categories. MLA based on lifestyle questionnaire only achieved AUC of 0.715 (95% CI 0.681, 0.750) and 0.710 (95% CI 0.653, 0.766) for low and high risk respectively. Combining all groups of risk factors (lifestyle survey questionnaires, clinical blood tests, 24-h ambulatory blood pressure and heart rate monitoring) along with feature selection, prediction of low and high CVD risk groups were further enhanced to 0.791 (95% CI 0.759, 0.822) and 0.790 (95% CI 0.745, 0.836). Besides conventional predictors, self-reported physical activity, average daily heart rate, awake blood pressure variability and percentage time in diastolic hypertension were important contributors to CVD risk classification.
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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.