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Arrhythmia classification using nearest neighbor search with principal component analysis
- Sun, Xiaolong;
- Park, Juyoung;
- Kang, Kyungtae
Citations
SCOPUS
2초록
Arrhythmia is currently classified by rate, mechanism, or duration, and many experts are using different techniques to classify arrhythmia. The present group of researchers have developed an automated method to select useful heartbeat features, which were then applied to a κ-nearest neighbor algorithm of arrhythmia classification. The arrhythmia dataset from the University of California, Irvine, Machine Learning Repository was applied to test the performance of our method, yielding a classification accuracy of 98%. Copyright is held by the author/owner(s).
키워드
Arrythmia classification; Principal component analysis; κ-nearest neighbour; Artificial intelligence; Bioinformatics; Classification (of information); Diseases; Information science; Learning systems; Nearest neighbor search; Statistical tests; Arrhythmia classification; Automated methods; Classification accuracy; Machine learning repository; Nearest neighbor algorithm; Nearest neighbour; University of California; Principal component analysis
- 제목
- Arrhythmia classification using nearest neighbor search with principal component analysis
- 저자
- Sun, Xiaolong; Park, Juyoung; Kang, Kyungtae
- 발행일
- 2015-09
- 유형
- Conference Paper
- 저널명
- BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
- 페이지
- 553 ~ 555
- 언어
- ENG
- 출판사
- Association for Computing Machinery, Inc
- 발행국가
- 미국
- 분량
- 3 페이지