ArticleBMC public health2019
Predicting coronary artery disease: a comparison between two data mining algorithms.
Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Trial
- An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.BMC medical informatics and decision making · 2026Article
- Toward Reliable Coronary Heart Disease Prediction: Integrating Multi-source Data with Ensemble Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Coronary artery disease diagnosis with signal processing and machine learning of heart sound signals: a systematic review.NPJ digital medicine · 2026Article
- 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
- Proteomic predictors of mortality among people with Coronary Artery Disease.American journal of preventive cardiology · 2025Article
- On the interpretability of the SVM model for predicting infant mortality in Bangladesh.Journal of health, population, and nutrition · 2024Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.European journal of medical research · 2024Article
- Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.European heart journal. Digital health · 2024Article
- Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.Frontiers in cardiovascular medicine · 2024Article
- Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia.Diagnostics (Basel, Switzerland) · 2023Article
- Studying Pregnancy Outcome Risk in Patients with Systemic Lupus Erythematosus Based on Cluster Analysis.BioMed research international · 2023Article
- Diagnosing Coronary Artery Disease on the Basis of Hard Ensemble Voting Optimization.Medicina (Kaunas, Lithuania) · 2022Article
- Forecasting COVID-19 cases using time series modeling and association rule mining.BMC medical research methodology · 2022Article
- Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms.Journal of geriatric cardiology : JGC · 2022Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Quality of life in patients with stable coronary artery disease submitted to percutaneous, surgical, and medical therapies: a cohort study.Health and quality of life outcomes · 2021Article
- Gene polymorphism of 3'APO-VNTR in Egyptians with coronary artery disease.Journal of medical biochemistry · 2021Article
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
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3 authors.
Funding
Abstract
backgroundCardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals.
methodsThe present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing.
resultsThe SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test's result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model.
conclusionAccording to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.
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