ArticleBMC medical informatics and decision making2022
Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 28 citations in OpenAlex.
- Comparing the Performances of Support Vector Machines and Artificial Neural Networks for Predicting Coronary Artery Diseases: A Cross-Sectional Study.Cardiology research and practice · 2026Article
- Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning.Annals of medicine · 2025Article
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Identifying risk factors associated with the health-related quality of life for coronary heart diseases elderly using association rule mining.Scientific reports · 2025Article
- Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.EJNMMI research · 2024Review
- Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.BMC medical informatics and decision making · 2024Article
- Article
- Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model.BMC cardiovascular disorders · 2022Article
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6 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeCoronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression. MATERIALS AND
methodsThis cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients.
resultsOut of the 758 examined cases, 250 (32.98%) cases were non-CAD and 508 (67.22%) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9%, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81%. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82%, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82%, respectively for the evaluation metrics.
conclusionsThe LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease.
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