ReviewHealthcare (Basel, Switzerland)2023
A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases.
Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.Archives of gynecology and obstetrics · 2026Pooled it
- An Edge-Cloud Collaborative ECG-Assisted Diagnostic System Leveraging Cross-Lead Knowledge Distillation and Large Language Models.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Genomic and Precision Medicine Approaches in Atherosclerotic Cardiovascular Disease: From Risk Prediction to Therapy-A Review.Biomedicines · 2025Review
- Predator crow search optimization with explainable AI for cardiac vascular disease classification.Scientific reports · 2025Article
- The role of artificial intelligence in cardiovascular research: Fear less and live bolder.European journal of clinical investigation · 2025Review
- Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data.Scientific reports · 2024Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems.Scientific reports · 2024Article
- How does the precise prediction of preeclampsia onset aid the overall management of preeclampsia?Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed.Sensors (Basel, Switzerland) · 2024Article
- Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study.Digital healthArticle
- Accelerating the performance of machine learning classifiers using bacterial colony optimization for heart disease prediction.Digital healthArticle
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
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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.