scholarly journals A database for using machine learning and data mining techniques for coronary artery disease diagnosis

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
R. Alizadehsani ◽  
M. Roshanzamir ◽  
M. Abdar ◽  
A. Beykikhoshk ◽  
A. Khosravi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Web Application ◽  
Disease Diagnosis ◽  
Data Mining Algorithms ◽  
Advance Research ◽  
Artery Disease ◽  
Mining Algorithms

Abstract We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports.

scholarly journals Diagnosing coronary artery disease via data mining algorithms by considering laboratory and echocardiography features

2013 ◽  
Vol 2 (3) ◽  
pp. 133 ◽  
Author(s):  
ZahraAlizadeh Sani ◽  
Roohallah Alizadehsani ◽  
Jafar Habibi ◽  
Hoda Mashayekhi ◽  
Reihane Boghrati ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Data Mining Algorithms ◽  
Artery Disease ◽  
Mining Algorithms

Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis

2012 ◽  
Vol 3 (1) ◽  
pp. 59-79 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Mohammad Javad Hosseini ◽  
Reihane Boghrati ◽  
Asma Ghandeharioun ◽  
Fahime Khozeimeh ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Major Type ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Diagnostic Modality ◽  
Data Mining Algorithms ◽  
Artery Disease

One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease (CAD) is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 and the results show that the SMO algorithm yielded very high sensitivity (97.22%) and accuracy (92.09%) rates.

Automated Diagnostics of Coronary Artery Disease

2012 ◽  
pp. 91-112
Author(s):  
Matjaž Kukar ◽  
Igor Kononenko ◽  
Ciril Grošelj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Coronary Artery Disease ◽  
Feature Extraction ◽  
Coronary Artery ◽  
Diagnostic Process ◽  
Long Term Study ◽  
Post Test ◽  
Artery Disease

The authors present results and the latest advancement in their long-term study on using image processing and data mining methods in medical image analysis in general, and in clinical diagnostics of coronary artery disease in particular. Since the evaluation of modern medical images is often difficult and time-consuming, authors integrate advanced analytical and decision support tools in diagnostic process. Partial diagnostic results, frequently obtained from tests with substantial imperfections, can be thus integrated in ultimate diagnostic conclusion about the probability of disease for a given patient. Authors study various topics, such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform the medical practice. During their long-term study (1995-2011) authors achieved, among other minor results, two really significant milestones. The first was achieved by using machine learning to significantly increase post-test diagnostic probabilities with respect to expert physicians. The second, even more significant result utilizes various advanced data analysis techniques, such as automatic multi-resolution image parameterization combined with feature extraction and machine learning methods to significantly improve on all aspects of diagnostic performance. With the proposed approach clinical results are significantly as well as fully automatically, improved throughout the study. Overall, the most significant result of the work is an improvement in the diagnostic power of the whole diagnostic process. The approach supports, but does not replace, physicians’ diagnostic process, and can assist in decisions on the cost-effectiveness of diagnostic tests.

scholarly journals Coronary Artery Disease Diagnosis: Ranking the Significant Features Using Random Trees Model

2020 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Edris Hassannataj Joloudari ◽  
Hamid Saadatfar ◽  
Mohammad Ghasemigol ◽  
Seyyed Mohammad Razavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Decision Tree ◽  
Disease Diagnosis ◽  
Random Trees ◽  
Support Vector ◽  
Interaction Detection ◽  
Artery Disease

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis by selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), the decision tree of C5.0, support vector machine (SVM), the decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

Machine learning-based coronary artery disease diagnosis: A comprehensive review

2019 ◽  
Vol 111 ◽  
pp. 103346 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Moloud Abdar ◽  
Mohamad Roshanzamir ◽  
Abbas Khosravi ◽  
Parham M. Kebria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Disease Diagnosis ◽  
Comprehensive Review ◽  
Artery Disease ◽  
Coronary Artery Disease Diagnosis

Automated Diagnostics of Coronary Artery Disease

Data Mining ◽  
2013 ◽  
pp. 1043-1063
Author(s):  
Matjaž Kukar ◽  
Igor Kononenko ◽  
Ciril Grošelj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Coronary Artery Disease ◽  
Feature Extraction ◽  
Coronary Artery ◽  
Diagnostic Process ◽  
Long Term Study ◽  
Post Test ◽  
Artery Disease

The authors present results and the latest advancement in their long-term study on using image processing and data mining methods in medical image analysis in general, and in clinical diagnostics of coronary artery disease in particular. Since the evaluation of modern medical images is often difficult and time-consuming, authors integrate advanced analytical and decision support tools in diagnostic process. Partial diagnostic results, frequently obtained from tests with substantial imperfections, can be thus integrated in ultimate diagnostic conclusion about the probability of disease for a given patient. Authors study various topics, such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform the medical practice. During their long-term study (1995-2011) authors achieved, among other minor results, two really significant milestones. The first was achieved by using machine learning to significantly increase post-test diagnostic probabilities with respect to expert physicians. The second, even more significant result utilizes various advanced data analysis techniques, such as automatic multi-resolution image parameterization combined with feature extraction and machine learning methods to significantly improve on all aspects of diagnostic performance. With the proposed approach clinical results are significantly as well as fully automatically, improved throughout the study. Overall, the most significant result of the work is an improvement in the diagnostic power of the whole diagnostic process. The approach supports, but does not replace, physicians’ diagnostic process, and can assist in decisions on the cost-effectiveness of diagnostic tests.

scholarly journals Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model

2020 ◽  
Vol 17 (3) ◽  
pp. 731 ◽  
Author(s):  
Javad Hassannataj Joloudari ◽  
Edris Hassannataj Joloudari ◽  
Hamid Saadatfar ◽  
Mohammad Ghasemigol ◽  
Seyyed Mohammad Razavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Decision Tree ◽  
Disease Diagnosis ◽  
Random Trees ◽  
Support Vector ◽  
Interaction Detection ◽  
Artery Disease

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.

Sign in / Sign up

Export Citation Format

Share Document