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Research questionHow can heterogeneous predictive models assess cardiovascular risk reliably from noisy real-time IoMT data?Continuous monitoring must infer cardiovascular risk from physiological measurements that may be noisy, incomplete, and produced by different sensors. Combining predictive models introduces additional challenges in producing consistent risk estimates as observations arrive.
AI
Health
Machine Learning
Neural and Evolutionary Computing
Statistical Machine Learning
Technology
Latest papersRecent research connected to this question, newest first.A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk AssessmentThe source concerns ECG, heart-rate, and blood-pressure data processed with noise reduction, normalization, missing-value imputation, feature selection, and ensembles involving SVM, Random Forest, XGBoost, and deep neural networks. It describes cloud-based real-time processing and reports results on real-world cardiovascular datasets, but does not provide details about prospective clinical deployment or validation.research paper · Sep 4, 2026
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