ArticleJournal of medical systems2016
A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.Computational and mathematical methods in medicine · 2022Pooled it
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
- An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm.Scientific reports · 2025Article
- Advanced supervised machine learning methods for precise diabetes mellitus prediction using feature selection.Frontiers in medicine · 2025Article
- An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management.Bioengineering (Basel, Switzerland) · 2024Article
- A distance-based kernel for classification via Support Vector Machines.Frontiers in artificial intelligence · 2024Article
- Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Artificial Intelligence in Resuscitation: A Scoping Review.Journal of clinical medicine · 2023Article
- Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system.Scientific reports · 2022Article
- The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction.Healthcare (Basel, Switzerland) · 2022Article
- Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study.BMC medical informatics and decision making · 2022Article
- Risk prediction of cardiovascular disease using machine learning classifiers.Open medicine (Warsaw, Poland) · 2022Article
- Machine learning models for screening carotid atherosclerosis in asymptomatic adults.Scientific reports · 2021Article
- Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing.Applied bionics and biomechanics · 2021Article
- Feature selection and risk prediction for patients with coronary artery disease using data mining.Medical & biological engineering & computing · 2020Article
- Changes in bowel sounds of inpatients undergoing general anesthesia.Biomedical engineering online · 2020Article
- Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.Scientific reports · 2020Article
- Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble.BioMed research international · 2020Article
- Predicting coronary artery disease: a comparison between two data mining algorithms.BMC public health · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary artery disease (CAD) is caused by atherosclerosis in coronary arteries and results in cardiac arrest and heart attack. For diagnosis of CAD, angiography is used which is a costly time consuming and highly technical invasive method. Researchers are, therefore, prompted for alternative methods such as machine learning algorithms that could use noninvasive clinical data for the disease diagnosis and assessing its severity. In this study, we present a novel hybrid method for CAD diagnosis, including risk factor identification using correlation based feature subset (CFS) selection with particle swam optimization (PSO) search method and K-means clustering algorithms. Supervised learning algorithms such as multi-layer perceptron (MLP), multinomial logistic regression (MLR), fuzzy unordered rule induction algorithm (FURIA) and C4.5 are then used to model CAD cases. We tested this approach on clinical data consisting of 26 features and 335 instances collected at the Department of Cardiology, Indira Gandhi Medical College, Shimla, India. MLR achieves highest prediction accuracy of 88.4 %.We tested this approach on benchmarked Cleaveland heart disease data as well. In this case also, MLR, outperforms other techniques. Proposed hybridized model improves the accuracy of classification algorithms from 8.3 % to 11.4 % for the Cleaveland data. The proposed method is, therefore, a promising tool for identification of CAD patients with improved prediction accuracy.
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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.