A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease

Aouabed, Zahia; Abdar, Moloud; Tahiri, Nadia; Gareau, Jaël Champagne

doi:10.1007/978-3-030-36778-7_53

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The highest accuracy of 97% was achieved by SVM linear kernel. Aouabed et al [ 58 ] developed an ensemble model for early detection of CAD. The ensemble model is based on four different kernel functions (linear, polynomial, radial basis, and sigmoid).…”

Section: State-of-the-art Workmentioning

confidence: 99%

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Javeed

Khan

Ali³

et al. 2022

Computational and Mathematical Methods in Medicine

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One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

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Section: State-of-the-art Workmentioning

confidence: 99%

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Javeed

Khan

Ali³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…An accuracy of 94.2% was attained using the deep learning method. In [18], a new ensemble model called "NE-nu-SVC (Nested Ensemble nu-SVC) was introduced for the detection of CAD.The proposed model was tested on two well-known CAD datasets: Z-Alizadeh and Sani. In [19], a multifilter approach was used to improve the performance of different decision trees (DTs) and then applied to the CAD dataset.…”

Section: Related Workmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

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Heart disease is one of the most common causes of death worldwide, and accurate and rapid diagnosis is needed to avoid serious complications. This study aimed to present a system for diagnosing and classifying common heart diseases using machine learning techniques. The data used are a set of medical records for heart patients at Ibb Medical Clinic, Ibb City, Yemen. Fourteen different classification algorithms were implemented using the Weka tool to classify different heart diseases, including eight heart diseases. The results showed that the RF algorithm was the best in terms of accuracy, sensitivity, specification, and F-measure. Based on this algorithm, a web system based on the ASPX language was designed to help users enter patient data and obtain a diagnosis and classification of heart disease. The system can help doctors in the diagnosis process and increase the medical awareness of the community.

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Section: Related Workmentioning

confidence: 99%

Classification and Diagnosis of Heart Disease Using Machine Learning

Mohsen‬‏,

Naoufel,

Alrashahy

et al. 2024

Preprint

View full text Add to dashboard Cite

Heart disease is a common and serious disease that causes many deaths around the world. The study aims to explore the use of machine learning techniques in classifying and diagnosing heart diseases and to develop a system capable of diagnosing and classifying different types of heart diseases using machine learning techniques. A number of algorithms commonly used in healthcare, such as Naive Bayes model, SVM, k-nearest neighbor (k-NN), and others, were reviewed. The study points out the importance of the quality of the data used in the database to obtain an accurate and reliable diagnosis. Data were collected from patient records in hospitals and clinics, analyzed and compared with previous relevant studies. Clinical decision assistance software has been used to help make medical decisions based on patient information. Positive results have been achieved that confirm the effectiveness of using machine learning techniques in diagnosing heart diseases. These technologies have shown the potential to improve the accuracy and efficiency of diagnosis, leading to improved patient outcomes and reduced health burdens. It also concluded the need to develop effective diagnostic tools and enhance the prevention of heart disease. The study is an important foundation for healthcare professionals and doctors working in the field of cardiology, as the techniques used can help them better understand and diagnose conditions and improve patient care.

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A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease

Cited by 6 publications

References 30 publications

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Classification and Diagnosis of Heart Disease Using Machine Learning

Classification and Diagnosis of Heart Disease Using Machine Learning

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