CADICA: Diagnosis of Coronary Artery Disease Using the Imperialist Competitive Algorithm

Mahmoodabadi, Zahra; Abadeh, Mohammad Saniee

doi:10.5626/jcse.2014.8.2.87

Cited by 8 publications

(6 citation statements)

References 10 publications

(5 reference statements)

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“…Work done in [16] compared Naïve Bayes, J48 decision trees and an artificial neural network(ANN) and saw that the Naïve Bayes algorithm performed the best. Setiawan et al [17], [18] also used the heart-disease directory datasets but they both employed fuzzy logic techniques, with [17] testing KNN and decision trees, among others.…”

Section: Sexmentioning

confidence: 99%

Machine Learning Classifications of Coronary Artery Disease

Nassif

Mahdi

Nasir

et al. 2018

2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

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Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool; however features and algorithms must be carefully selected to get accurate classification. To this effect, three feature selection methods have been used on 13 input features from the Cleveland dataset with 297 entries, and 7 were selected. The selected features were used to train three different classifiers, which are SVM, Naïve Bayes and KNN using 10-fold cross-validation. The resulting models evaluated using Accuracy, Recall, Specificity and Precision. It is found that the Naïve Bayes classifier performs the best on this dataset and features, outperforming or matching SVM and KNN in all the four evaluation parameters used and achieving an accuracy of 84%.

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Section: Sexmentioning

confidence: 99%

Machine Learning Classifications of Coronary Artery Disease

Nassif

Mahdi

Nasir

et al. 2018

2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

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“…This can lead to chest pain (angina), or a heart attack (myocardial infarct). A heart attack occurs when at least one coronary artery is completely blocked [3].…”

Section: Introductionmentioning

confidence: 99%

“…Angiography is the most reliable method among the various methods of diagnosis for CAD. However, angiography is a costly and invasive procedure [3]. Moreover, various studies have shown that angiography may result in complications [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Golpour

Ghayour‐Mobarhan

Saki

et al. 2020

IJERPH

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(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

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“…It has a better convergence rate compared to other optimization algorithms. Recently, ICA has been used in optimization algorithms, dimensionality reduction, and feature selection in many research topics such as medical research (8), (9), and (10).…”

Section: Introductionmentioning

confidence: 99%

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

Masoudiasl

Vahdat

Hessam

et al. 2019

Iran Red Crescent Med J

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Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional approaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical research to distinguish infected individuals from healthy ones, accurately. Objectives: In this study, we aim to develop an ensemble machine learning (ML) method to distinguish tumor samples from healthy samples robustly. Methods: We used an Imperial Competitive Algorithm coupled with a Fuzzy System (ICA-Fuzzy-SR) to identify the most influencing features to recognize tumor samples. To evaluate the proposed method, we used the publicly available Wisconsin Breast Cancer Dataset (WBCD). Results: Benchmarking with the current existing leading methods indicates that our proposed method achieves 95.45% prediction accuracy, which is 3% better than those reported in previous studies. Conclusions: Such results achieve while our model is significantly faster than previously proposed models to solve this problem.

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CADICA: Diagnosis of Coronary Artery Disease Using the Imperialist Competitive Algorithm

Cited by 8 publications

References 10 publications

Machine Learning Classifications of Coronary Artery Disease

Machine Learning Classifications of Coronary Artery Disease

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer

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