Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree

Mohebbanaaz,; Kumari, L. V. Rajani; Sai, Y. Padma

doi:10.1007/s11760-021-02009-x

Cited by 61 publications

(12 citation statements)

References 29 publications

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“…(7) In the process of completing the task, if the learner encounters a problem, he can post the problem to the knowledge explanation module of the system to seek answers and problem-solving strategies. (8) Let the students take off the earphones to discuss in groups of men and women. (9) The teacher invites the group to demonstrate the dialogue and express the experience, so as to summarize the class, arrange the post-class consolidation and preview the content for the next section.…”

Section: Teaching Process Designmentioning

confidence: 99%

“…linguistics, ignore the principles and methods of the teaching process, and ignore the irreplaceable nature of teachers. However, the role of English teachers is not only to teach language and grammar knowledge, or to let students master language communication tools, but also to use language tools to teach different cultural essences, spread life philosophy, stimulate the desire for knowledge and exploration of the world, and achieve Its own value, rather than just making a living with language tools [1] [8].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Processing of English Education Model Based on Artificial Intelligence Technology

Zhang¹

2023

Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

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With the rapid development of science and technology, the society has entered a new era of knowledge and information. Modern information teaching has changed the traditional curriculum concept, improved the curriculum model and improved the teaching method in the curriculum field. This paper studies the optimization of English education mode based on artificial intelligence technology, mainly using SAIES system, so that middle school students are placed in an intelligent environment. The practice of SAIES system in middle school English teaching can be divided into six steps. The teaching process of SAIES system consists of ten steps. Finally, the reliability and validity of the system are tested. The results show that the overall reliability of the learning adaptability level questionnaire is 0.913, and the reliability of the system is very good.

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Section: Teaching Process Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Processing of English Education Model Based on Artificial Intelligence Technology

Zhang¹

2023

Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

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“…Abnormal classification has become a valuable and promising technique for early assessment of arrhythmia. Mohebbanazz et al, [18] proposed an optimized decision tree (DT) and adaptive boosted optimized decision tree method for classification of six types of ECG beats and evaluated the performance of the model on MITBIH arrhythmia database achieved an effective accuracy of 98.77% compared to stateof-the-art techniques. [19] introduced a time-efficient, reliable, and low-complexity resource-saving architecture with random forest (RF) classifier to classify two major types of arrhythmias such as supraventricular ectopic beats ventricular ectopic beats (VEB) and (SVEB).…”

Section: Motivation Towards Ensemble Learningmentioning

confidence: 99%

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Ardeti¹,

Kolluru²,

Varghese³

et al. 2022

IJACSA

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Automated classification of each heartbeat class from the ECG signal is important to diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the realtime or clinical databases contains exceptional values or extreme values called outliers. The separation and removal of outliers is very much useful for improving the data quality. The presence of outliers will influence the results of machine learning (ML) methods such as classification and regression. Outlier identification and removal plays a significant role in this area of research and is a part of signal denoising. Also, most of the traditional ECG-signal processing methods are facing the difficulty in finding the essential key features of recorded signal. In this work, an extreme outlier detection technique known as improved inter quartile range (IIQR) filtering method is used to find the outliers of the signal for the feature ranking process. In addition, an optimized random forest (ORF) based heterogenous ensemble classification model is proposed to improve the true positive and runtime on the ECG data. The classification of each heartbeat type is classified with majority voting technique. Ensemble learning and majority voting rule is used to enhance the accuracy of heart disease prediction. The proposed feature ranking based ORF ensemble classification model (LR + SVM + ORF + XGBoost + KNN) is evaluated on the MITBIH arrhythmia database and produces an overall accuracy of 99.45% which significantly outperforms the state-of-the-art methods such as, (LR + SVM + RF + XGBoost + KNN) with 96.17% accuracy, ensemble deep learning accuracy of 95.81% and ensemble SVM accuracy of 94.47%.

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“…and isometric scales (number of classes, number of online discussions, etc.) [9][10]. First, the various implications of these analytic variables for different courses, students with different learning outcomes, and the potential relationships between each analytic variable were identified by means of categorical analysis in data mining.…”

Section: Research Architecturementioning

confidence: 99%

Application and Optimization of Decision Tree Algorithm in Classification Prediction

2021

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In online teaching situations, a decision tree (DT) analysis algorithm is proposed in order to enable teachers to track students' learning behaviors and learning status in a timely manner. With the help of this algorithm, teachers can use the existing learning record files to analyze students' learning behaviors through multiple observation dimensions. In this paper, by constructing a learning achievement classification prediction (CP) model based on ID3 decision tree algorithm (DTA), the optimized ID3 DTA is used to analyze students' learning achievement and predict students who may not reach the expected teaching goals, so that teachers can give appropriate teaching assistance in time to achieve teaching goals. Through experiments, it is proved that with the increase of data volume, the accuracy of the model for CP of learning achievement is stable above 80%, which realizes the function of ID3 algorithm achievement prediction.

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Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree

Cited by 61 publications

References 29 publications

Optimal Processing of English Education Model Based on Artificial Intelligence Technology

Optimal Processing of English Education Model Based on Artificial Intelligence Technology

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Application and Optimization of Decision Tree Algorithm in Classification Prediction

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