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학술지 Dual-Phase Approach to Improve Prediction of Heart Disease in Mobile Environment
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저자
이양구, Thi Hong Nhan Vu, Thanh Ha Le
발행일
201504
출처
ETRI Journal, v.37 no.2, pp.222-232
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.15.2314.0103
협약과제
14MC3500, 병사들에게 실전과 같은 가상훈련 환경을 제공하기 위한 전 방향 이동 지원 상호작용 소프트웨어 기술 개발, 박상준
초록
In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.
키워드
Healthcare service, Heart disease, Neural network, Prediction, Rule-based classification
KSP 제안 키워드
Artificial neural networks, Diagnostic accuracy, Disease prediction, Dual-phase, Healthcare Services, Heart rate variability, Long period, Prediction of heart disease, Prediction process, Rule-Based Classification, Self-organizing Map