SynthesisCurrent cardiology reviews2023
Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.
Synthesis in Current cardiology reviews, 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.
What it found
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Cardiovascular Disease Knowledge Amongst Jordanian Population: A Cross-Sectional Study.Health science reports · 2026Article
- Narrative review of the development of an ischaemic heart disease prognostic scoring tool (i-IHD score) among patients with type 2 diabetes mellitus in Malaysia.Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia · 2026Review
- Comparative performance of machine learning and deep learning models for heart disease prediction in a small clinical dataset.American journal of cardiovascular disease · 2026Article
- Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review.Healthcare informatics research · 2025Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Identification and verification of feature biomarkers associated with CD14Journal of diabetes investigation · 2025Article
- Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.JMIR cardio · 2025Article
- Optimizing Revascularization in Ischemic Cardiomyopathy: Comparative Evidence on the Benefits and Indications of CABG and PCI.Life (Basel, Switzerland) · 2025Review
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment.Contemporary clinical trials communications · 2023Article
- Article
- Perceptions, barriers, and risk of artificial intelligence Among healthcare professionals: A cross-sectional study.Digital healthArticle
- Exploring the Level of Self-Care Behavior, Motivation, and Self-Efficacy among Individuals With Hypertension: A Cross-Sectional Study.SAGE open nursingArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.
methodsThis systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore.
resultsThirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning.
conclusionApplying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.
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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.