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All Journal Journal of Telenursing (JOTING)
Tutut Riana Hapsari
Universitas Indonesia
Health Professions Nursing

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Deteksi Penyakit Kardiovaskular pada Isyarat EKG Berbasis Deep Learning Tutut Riana Hapsari; Lestari Sukmarini; Tuti Herawati
Journal of Telenursing (JOTING) Vol 5 No 1 (2023): Journal of Telenursing (JOTING)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/joting.v5i1.5178

Abstract

The purpose of this study is to review studies related to the CNN method in predicting cardiovascular disease so that it can explain the comparison of results between the CNN studies to detect cardiovascular disease that have been conducted. The research method used is a literature review. The results of the study show that the use of the CNN method to detect cardiovascular disease has excellent results, with the highest percentage of accuracy reaching 99.79%, the highest percentage of F1-score achieving 99.78%, the highest percentage of specificity achieving 98.35%, and the highest sensitivity reaching 99.71%. In conclusion, the CNN method can be implemented into a clinical decision support system that health workers such as doctors and nurses can use to assess patient health. Further research must consider the proposed CNN architectural model using the database to outperform existing research. Keywords: Convolutional Neural Network, Electrocardiogram, Detection of Cardiovascular Disease
Co-Authors Lestari Sukmarini Tuti Herawati