A data mining approach for diagnosis of coronary artery disease

Alizadehsani, Roohallah; Habibi, Jafar; Hosseini, Mohammad Javad; Mashayekhi, Hoda; Boghrati, Reihane; Ghandeharioun, Asma; Bahadorian, Behdad; Sani, Zahra Alizadeh

doi:10.1016/j.cmpb.2013.03.004

Cited by 257 publications

(146 citation statements)

References 8 publications

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“…This dataset contains 303 records with 54 features. Alizadehsani et al aimed to classify patients into two target classes: suffered by coronary artery disease or normal [20]. They divided the original set of variables into four groups: demographic, symptoms and examination, ECG, laboratory, and echo.…”

Section: A Related Workmentioning

confidence: 99%

Predictive and Descriptive Analysis for Heart Disease Diagnosis

Babič

Olejár

Vantova

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-The heart disease describes a range of conditions affecting our heart. It can include blood vessel diseases such as coronary artery disease, heart rhythm problems or and heart defects. This term is often used for cardiovascular disease, i.e. narrowed or blocked blood vessels leading to a heart attack, chest pain or stroke. In our work, we analysed three available data sets: Heart Disease Database, South African Heart Disease and Z-Alizadeh Sani Dataset. For this purpose, we focused on two directions: a predictive analysis based on Decision Trees, Naive Bayes, Support Vector Machine and Neural Networks; descriptive analysis based on association and decision rules. Our results are plausible, in some cases comparable or better as in other related works

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Section: A Related Workmentioning

confidence: 99%

Predictive and Descriptive Analysis for Heart Disease Diagnosis

Babič

Olejár

Vantova

et al. 2017

Annals of Computer Science and Information Systems

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“…The results of this comparison showed that the C statistics of the best ANN ensemble and the best logistic regression model were 0.81 and 0.76, respectively (* p =0.03) 24. Similarly, Alizadehsani et al 25 used data mining for the diagnosis of CAD and showed that characteristic chest pain, region RWMA2, and age were the most effective features, in addition to the features created using Information Gain. In addition, using this method and the feature creation algorithm, 94.08% accuracy was achieved, which is higher than current approaches in the literature 25.…”

Section: Discussionmentioning

confidence: 95%

“…Similarly, Alizadehsani et al 25 used data mining for the diagnosis of CAD and showed that characteristic chest pain, region RWMA2, and age were the most effective features, in addition to the features created using Information Gain. In addition, using this method and the feature creation algorithm, 94.08% accuracy was achieved, which is higher than current approaches in the literature 25. Future studies could use data mining to develop PTP calculation models for CAD.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review

Liu

et al. 2017

Clinics

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A comprehensive search of PubMed and Embase was performed in January 2015 to examine the available literature on validated diagnostic models of the pre-test probability of stable coronary artery disease and to describe the characteristics of the models. Studies that were designed to develop and validate diagnostic models of pre-test probability for stable coronary artery disease were included. Data regarding baseline patient characteristics, procedural characteristics, modeling methods, metrics of model performance, risk of bias, and clinical usefulness were extracted. Ten studies involving the development of 12 models and two studies focusing on external validation were identified. Seven models were validated internally, and seven models were validated externally. Discrimination varied between studies that were validated internally (C statistic 0.66-0.81) and externally (0.49-0.87). Only one study presented reclassification indices. The majority of better performing models included sex, age, symptoms, diabetes, smoking, and hyperlipidemia as variables. Only two diagnostic models evaluated the effects on clinical decision making processes or patient outcomes. Most diagnostic models of the pre-test probability of stable coronary artery disease have had modest success, and very few present data regarding the effects of these models on clinical decision making processes or patient outcomes.

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“…Healthcare department is commonly believed that "information rich" and "knowledge-poor". Alizadehsani, et al [6] conducted experiment Cardiovascular disease is often very rare and is the important reason of decease. The fundamental sort of these sicknesses as Coronary Artery Disease (CAD) and the determination is essential.…”

Section: Review Of Literaturementioning

confidence: 99%

Data Mining Techniques to Construct a Model: Cardiac Diseases

Akhtar¹,

Talib²,

Kanwal³

2018

ijacsa

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Abstract-Using echocardiography flexible TransthoracicEchocardiography reported data set detecting heart disease by using mining techniques designed prediction model the data set can develop the reliability of analysis of cardiac diseases by echocardiography, using eight iterative and interactive steps consisting Knowledge Discovery in Database (KDD) methodology including from 209 patients with echocardiography to extracting the data important mode of action Transthoracic Echocardiography inspection report. This study used data from Faisalabad Institute of Cardiology study from 2012 to 2015. All models exposed the results of J48 decision tree, naïve bayes classifier and neural network that has extraordinary classification precision and predictive of heart disease cases are generally comparable. However, J48 model predictive classification accuracy shows of 80% based on the true positive rate ratio and performance slightly better. This study shows to predict heart disease cases and People can be used the results of our study to make more consistent diagnosis of cardiac disease and to help them as a support tool for cardiac disease specialists.

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A data mining approach for diagnosis of coronary artery disease

Cited by 257 publications

References 8 publications

Predictive and Descriptive Analysis for Heart Disease Diagnosis

Predictive and Descriptive Analysis for Heart Disease Diagnosis

Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review

Data Mining Techniques to Construct a Model: Cardiac Diseases

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