Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach

Hedeshi, Najmeh Ghadiri; Abadeh, Mohammad Saniee

doi:10.1155/2014/783734

Cited by 35 publications

(4 citation statements)

References 20 publications

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“…Results showed that GA was useful for autotuning of the CANFIS parameters. Hedeshi and Abadeh [ 28 ] performed PSO algorithm with a boosting approach. The proposed method used fuzzy rule extraction with PSO and enhanced-particle swarm optimization 2 (En-PSO2).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Nalluri

Kannan

Manisha

et al. 2017

Journal of Healthcare Engineering

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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Section: Related Workmentioning

confidence: 99%

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Nalluri

Kannan

Manisha

et al. 2017

Journal of Healthcare Engineering

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“…Coronary artery disease (CAD) is a common chronic disease in which the coronary arteries become narrowed by atherosclerosis, resulting in an inadequate blood supply to the heart muscle [1,2]. Narrowing of the coronary arteries can lead to sudden blockage of the coronary arteries, resulting in myocardial infarction or even sudden cardiac death [3,4]. The World Health Organization has identified CAD as the leading cause of death in both developing and developed countries [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…However, more than 60% of the global burden of CAD occurs in developing countries [7]. CAD is also the leading cause of death in the United States [3]. In China, an estimated 290 million people suffer from heart disease, and the death rate from heart disease is more than 40% [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Coronary Artery Diseases Screening: A Comprehensive Assessment of Machine Learning Approaches Using Routine Clinical and Laboratory Data.

Naji,

Niazkhani,

Khadem vatan

et al. 2024

Front Health Inform

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Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data.Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning—were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model.Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model.Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.

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“…Additionally, manual feature extraction has similar problems as in group-(a); these studies must follow standardized guidelines to compare performance consistently. Due to the availability of several datasets with clinical parameters, the studies in group-(c) are prevalent among scientists [38][39][40][41][42][43][44][45][46][47][48][49][50]. Here, a specific list of clinical parameters is used as input for the system; the number and types of parameters depend on which dataset is selected.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Mamun

Elfouly

2023

Bioengineering

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Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

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Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach

Cited by 35 publications

References 20 publications

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Enhancing Coronary Artery Diseases Screening: A Comprehensive Assessment of Machine Learning Approaches Using Routine Clinical and Laboratory Data.

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

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