Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor

Mohamad Sabri Bin Sinal, Eiji Kamioka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heart diseases contribute to the highest cause of death around the world particularly for middle aged and elderly people. There are various types of heart disease symptoms. One of the most common types is Arrhythmia which is considered as a dangerous heart condition since the symptom itself may initiate more chronic heart diseases and result in death if it is not treated earlier. However, the detection of Arrhythmia by humans is regarded as a challenging task because the natures of the symptom appear at random times. Therefore, an automatic detection method of abnormal heartbeat in ECG (electrocardiogram) data is needed to overcome the issue. In this paper, a novel multistage classification approach using K-Nearest Neighbor and decision tree of the 3 segments in the ECG cycle is proposed to detect Arrhythmia heartbeat from the early minute of ECG data. Specific attributes based on feature extraction in each heartbeat are used to classify the Normal Sinus Rhythm and Arrhythmia. The experimental result shows that the proposed multistage classification approach is able to detect the Arrhythmia heartbeat with 90.6% accuracy for the P and the Q peak segments, 91.1% accuracy for the Q, R and S peak segments and lastly, 97.7% accuracy for the S and the T peak segments, outperforming the other data mining techniques.

Original languageEnglish
Title of host publicationAICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference
PublisherAssociation for Computing Machinery
Pages29-34
Number of pages6
ISBN (Electronic)9781450366236
DOIs
Publication statusPublished - 2018 Dec 21
Event2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018 - Tokyo, Japan
Duration: 2018 Dec 212018 Dec 23

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018
CountryJapan
CityTokyo
Period18/12/2118/12/23

Keywords

  • Computational Intelligence
  • Data Mining
  • Heart Disease
  • Heart Disease Classification
  • Heartbeat Classification
  • Machine Learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Bin Sinal, M. S., & Kamioka, E. (2018). Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. In AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference (pp. 29-34). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3299819.3299848