Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi, Qurat-ul-ain; Wah, Teh Ying; Raj, Ram Gopal; Iqbal, Uzair

doi:10.1155/2018/2016282

Cited by 48 publications

(30 citation statements)

References 59 publications

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“…МО с учителем используют заранее размеченные наборы данных, обработка которых позволяет выделить факторы, оказывающие влияние на клиническое течение ИБС и ее прогноз [15]. Для реализации этих задач помимо "классической" ЛР используют такие методы МО, как деревья решений (ДР), случайный лес (СЛ), наивный байесовский классификатор (НБК), машины опорных векторов (SVM), k-ближайших соседей (kNN), каждый из которых имеет свои преимущества и недостатки [20]. В настоящее время к наиболее перспективным методам МО относят глубокое обучение (ГО), которое реализуется с помощью многослойных искусственных нейронных сетей (ИНС).…”

Section: автоматизированные системы и публичные наборы данныхunclassified

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

et al. 2020

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Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnosing and predicting the clinical course of coronary artery disease. We provided information on reference databases, the use of which allows to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs.

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Section: автоматизированные системы и публичные наборы данныхunclassified

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

et al. 2020

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“…Mastoi, Qurat-ul-ain, et al [10] investigated the automation of CAD diagnosis by the ML algorithms such as k- Hampe, Nils, et al [11] have proposed the exploration of cardiac computed tomography (CT) visualization by ML algorithms. CT generates detailed high spatial resolution with hundreds of slices which are under-utilized due to paucity of trained cardiac imagers and overwhelming workload for medical professionals.…”

Section: Review Of Related Workmentioning

confidence: 99%

Machine Learning and Statistical Approaches for Classification of Risk of Coronary Artery Disease using Plasma Cytokines.

Saharan

Nagar

Creasy

et al. 2020

Preprint

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BackgroundAs per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically very expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, it can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, k-fold cross validation for hyperparameter tuning, feature selection via feature importance identification were integrated within the models.ResultsA total of 5 classifiers were developed, with two built using 35 cytokine predictive features and three built using a subset of cytokines, selected by variable importance techniques namely Random Forest, ReliefF and Boruta. The best Area under Receiver Operating Characteristic (AUROC) based accuracy of .99 was achieved by the Random Forest classifier with 35 cytokine biomarkers. The second-best AUROC accuracy was achieved by the k-NN model using cytokines selected by the Random Forest variable importance selection mechanism.ConclusionsPresently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to the conventional treatments such as angiography. The early detection can be further improved by incorporating 65 novel and sensitive cytokines biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

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“…As the number of IoT connected devices are set to increase rapidly, the use of 5G for data acquired from IoT, IoNT, IoMT devices is highly required to transmit and process the data. This can be achieved as a result of nano-network communication can be in form of nano-molecular, chemical, mechanical and electromagnetic communication [97].…”

Section: Introductionmentioning

confidence: 99%

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview

Ibrahim

Al‐Turjman²,

Said

et al. 2020

Multimed Tools Appl

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Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPRbiosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPRbased biosensors with high sensitivity of Attomolar (10 −18 M), Femtomolar (10 −15 M) and Picomolar (10 −12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10 −9 M and micromolar 10 −3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

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Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Cited by 48 publications

References 59 publications

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

Machine Learning and Statistical Approaches for Classification of Risk of Coronary Artery Disease using Plasma Cytokines.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview

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