Diagnosis of coronary artery disease based on machine learning algorithms support vector machine, artificial neural network, and random forest

Saeedbakhsh, Saeed; Sattari, Mohammad; Mohammadi, Maryam; Najafian, Jamshid; Mohammadi, Farzaneh

doi:10.4103/abr.abr_383_21

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…The study conducted by Ayatollahi H et al [34], aimed at comparing the performance of SVM and ANN models in predicting CAD, showed that the accuracy of SVM model is higher than that of ANN model. Similarly, the study by Saeedbakhsh S et al [35], which aimed at detecting CAD using machine learning algorithms, showed that the accuracy of SVM was 88.8% and for ANN it was 88.5%. In Kumar R et al [36].…”

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confidence: 88%

“…Our ndings indicate that the sensitivity for predicting CAD is higher for SVM and ANN models compared to the LR model, but SVM and ANN models are equal. In Nusinovici [35] showed that the sensitivity of SVM and ANN models was 0.574 and 0.582, respectively. These results are similar to our ndings and indicate that SVM and ANN models have similar sensitivity indices.…”

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confidence: 99%

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Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

Shariatnia

Rajabi

Ziaratban

et al. 2023

Preprint

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Background Coronary artery disease (CAD) is considered as an inflammatory disease. Cardiovascular disease (CVD) is a major cause of death and disability worldwide. This study aimed to compare the performance of different non-invasive CAD diagnostic techniques. Methods A cross-sectional study was performed on a total of 758 subjects (250 with CAD and 508 without CAD). We compared the performances of logistic regression (LR) model, artificial neural networks (ANN), and support vector machines (SVMs) for the purpose of functioning. The Performance of classification techniques were compared using ROC curve, sensitivity, specificity, and accuracy. Results The study population consisted of 758 case subjects. Two hundred fifty of them (33.6% men and 66.4% women) were eventually diagnosed with non-CAD, while 508 subjects (64% men and 36% women) were not (33.6% men and 66.4% women). The area under the ROC Curve (AUC) for CAD resulted in 0.775 (95% CI: 0.711, 0.838) for Logistic regression model, 0.752 (95% CI: 0.682, 0.823) for ANN, and 0.793 (95% CI: 0.733, 0.853) for SVMs, respectively. There were significant differences between these three models in prediction of CAD (p = 0.04). The best model of forecasting CAD was the SVMs (0.793, 95% CI: 0.733, 0.853). However, the differences between logistic regression model, ANN and LR with SVMs were small and non-significant (p = 0.2, p = 0.09). Conclusions Support vector machines (SVMs) yielded better performance than ANN model to predict the risk of coronary artery disease (CAD) with simple clinical predictors. However, support vector machines produced as much performance as the LR model.

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confidence: 88%

Section: <> Discussionmentioning

confidence: 99%

Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

Shariatnia

Rajabi

Ziaratban

et al. 2023

Preprint

View full text Add to dashboard Cite

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Comparing Support Vector Machines and Neural Networks for Cardiac Arrest Risk Prediction

Chaudhary,

Agrawal,

Saraswat

et al. 2024

2024 2nd International Conference on Disruptive Technologies (ICDT)

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Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques

Mansoor,

Chettri,

Naleer

2024

THC

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BACKGROUND: Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease. OBJECTIVE: The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context. METHODS: A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc. RESULTS: When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields. CONCLUSION: Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.

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Diagnosis of coronary artery disease based on machine learning algorithms support vector machine, artificial neural network, and random forest

Cited by 3 publications

References 34 publications

Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

Comparing Support Vector Machines and Neural Networks for Cardiac Arrest Risk Prediction

Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques

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