Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction

Al-Yarimi, Fuad Ali Mohammed; Munassar, Nabil Mohammed Ali; Al‐Wesabi, Fahd N.

doi:10.1108/dta-03-2020-0076

Cited by 4 publications

(3 citation statements)

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“…Though the ADCA is an ensemble classification model, it does not address the false alarming caused by the high dimensionality of the values representing the training phase's features. Our earlier contribution of a classification technique, Electrocardiogram Stream Level Correlated Patterns as Features (ESCPF) [42], addressed a novel feature selection and feature optimization methods to perform heartbeat classification to identify the arrhythmia scope in a given electrocardiogram. However, the false alarm due to dimensionality in feature values has not been addressed by ESCPF.…”

Section: Related Researchmentioning

confidence: 99%

“…A computerized learning method, association rule mining (Apriori), is used to spot factors [51]. Automatic Detection of Cardiac Arrhythmias (ADCA) Using Ensemble Learning [41] and Electrocardiogram Stream level Correlated Patterns as Features (ESCPF) [42]. The graph has been plotted among metric specificity and ten folds of leave pair out cross-validation performed on ELAP, ADCA, and ESCPF models, as shown in Fig.…”

Section: Experimental Studymentioning

confidence: 99%

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Handling High Dimensionality in Ensemble Learning for Arrhythmia Prediction

Al-Yarimi¹

2022

Intelligent Automation &Amp; Soft Computing

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Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models' training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The proposed method is working as a classification approach that incorporates both supervised and unsupervised learning methods. The experimental study addresses the rise of the proposed method in the prediction accuracy of both labels. The cross-validation statistics of ELAP have been compared to contemporary methods to scale the performance of the ELAP. The proposed method ELAP. Concerning scale, the prediction accuracy, the scope of the false alarm, the robustness of the label prediction, the outcomes of the assessment metrics obtained from 10-fold leave pair out cross-validation performed on proposed ELAP has compared to the corresponding outcomes metrics obtained from the contemporary methods.

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Section: Related Researchmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Handling High Dimensionality in Ensemble Learning for Arrhythmia Prediction

Al-Yarimi¹

2022

Intelligent Automation &Amp; Soft Computing

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“…Алгоритм AdaBoost создает ансамбль слабых классификаторов, вызываемых каскадно, каждое следующее дерево исправляет ошибки предыдущего, а предсказания основаны на совокупности деревьев. Алгоритм AdaBoost успешно применяется при исследовании данных в области здравоохранения [20], кредитования [21], выявления мошеннических действий с пластиковыми картами [22,23].…”

Section: деревья решенийunclassified

Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients

Eketnova

2021

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In the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institutions

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