The classification accuracy for the single- (light gray) and the ensemble classifiers (dark gray).

<p>The ensemble classifier has outperformed the single classifier for both the decision tree (DT) and the SVM classifier types. The error bars represent the 95% confidence interval for the classification accuracy.</p>

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Bibliographic Details
Main Author:	Mehdi Hosseinzadeh (8383239) (author)
Other Authors:	Amir Haider (20490474) (author), Mazhar Hussain Malik (17081487) (author), Mohammad Adeli (20490477) (author), Olfa Mzoughi (17088834) (author), Entesar Gemeay (19515637) (author), Mokhtar Mohammadi (11792213) (author), Hamid Alinejad-Rokny (518381) (author), Parisa Khoshvaght (20490480) (author), Thantrira Porntaveetus (140198) (author), Amir Masoud Rahmani (11553799) (author)
Published:	2024
Subjects:	Medicine Biotechnology Ecology Developmental Biology Cancer Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machine k nearest neighbors individually assess beats 64 %, implying nine consecutive beats heart sound classification ensemble classifier strategy classify heart sounds thirteen mfccs estimated single classifier strategy overall classification based nine knn classifiers either nine svms abnormal heart sounds heart sounds single classifier nine dts single vs xlink "> similar studies results demonstrated remove noise paper seeks majority vote knn ), important cues first pre diastole intervals decision tree comparative analysis
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The classification accuracy for the single- (light gray) and the ensemble classifiers (dark gray).

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