A new machine learning technique for an accurate diagnosis of coronary artery disease

Abdar, Moloud; Książek, Wojciech; Acharya, U. Rajendra; Tan, Ru San; Makarenkov, Vladimir; Pławiak, Paweł

doi:10.1016/j.cmpb.2019.104992

Cited by 216 publications

(89 citation statements)

References 58 publications

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“…Therefore, clinical informatics has been carefully used for analyzing the EHR data and accurately classifying the disease based on machine learning algorithms and statistical techniques. For this purpose, recent works have applied classification algorithms such as Decision Trees (DT) and Naive Bayes for Heart disease prediction [11], and K-Nearest Neighbor (KNN) for an automatic classification of the blood pressure [12]. Another work conducted three types of SVM classifiers for predicting the Coronary artery disease [13].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…In a study conducted by Abdar et al [40], used two-level hybrid genetic algorithm and NuSVM called N2Genetic-NuSVM. Given two-level genetic algorithm, it is used to optimize the SVM Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 January 2020 doi:10.20944/preprints202001.0220.v1…”

Section: Related Workmentioning

confidence: 99%

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

Joloudari¹,

Joloudari²,

Saadatfar³

et al. 2020

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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.

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“…[24][25][26][27][28] They have also been used for treating cardiovascular diseases. [29][30][31][32][33] MLT's can be proposed as good candidates to identify different material model parameters, and we strongly believe that the use of these mathematical tools could successfully help to improve the characterization of soft biological tissues. Moreover, the use of MLTs also presents certain advantage in terms of computational costs, reducing computation time in comparison to gradient-base methods, where this time becomes indefinite, searching for an appropriate initial seed.…”

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confidence: 99%

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2020

IJBSBE

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A new machine learning technique for an accurate diagnosis of coronary artery disease

Cited by 216 publications

References 58 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

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

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