RETRACTED ARTICLE: Time series real time naive bayes electrocardiogram signal classification for efficient disease prediction using fuzzy rules

Towards the problem of ECG classification and disease prediction, various approaches are analyzed and discussed. However, the methods suffer to achieve higher performance in classification or disease prediction. To improve the performance, an efficient time series real time Naive Bayes ECG classific...

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Bibliographic Details
Published inJournal of ambient intelligence and humanized computing Vol. 12; no. 5; pp. 5257 - 5267
Main Authors Aarthy, S. T., Iqbal, J. L. Mazher
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Cardiac arrhythmia
Classification
Computational Intelligence
Conditional probability
Deep learning
Electrocardiography
Engineering
Feature extraction
Heart
Machine learning
Neural networks
Original Research
Performance enhancement
Performance prediction
Real time
Robotics and Automation
Signal classification
Signal processing
Time series
User Interfaces and Human Computer Interaction
Wavelet transforms
ECG
Signal processing
Fuzzy rule
Naïve bayes classifier
Disease prediction
Time series data
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Summary:Towards the problem of ECG classification and disease prediction, various approaches are analyzed and discussed. However, the methods suffer to achieve higher performance in classification or disease prediction. To improve the performance, an efficient time series real time Naive Bayes ECG classification and disease prediction approach using fuzzy rule is presented in this paper. The method reads the ECG signals available and performs noise removal initially. From the graphs available, the features mentioned above are extracted and if there exist any incomplete or missing signal then the ECG sample has been removed from the data set. Once the preprocessing and feature extraction are done, then the features extracted. With the learned features, the method generates fuzzy rule for different disease class. The proposed algorithm computes posterior probability according to the mapping of different features of fuzzy rule. The classification or disease prediction is performed by measuring multi-feature signal similarity (MFSS). Estimated MFFS value has been used to measure the cardiac disease prone weight (CDPW) towards various classes available. According to the value of CDPW has been used to perform classification or disease prediction.
Bibliography:retraction
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02003-0