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Dual-Phase Approach to Improve Prediction of Heart Disease in Mobile Environment

  • Lee, Yang Koo (IT Convergence Technology Research Laboratory, ETRI) ;
  • Vu, Thi Hong Nhan (Faculty of Information Technology, UET, Vietnam National University) ;
  • Le, Thanh Ha (Faculty of Information Technology, UET, Vietnam National University)
  • Received : 2014.08.09
  • Accepted : 2015.01.17
  • Published : 2015.04.01

Abstract

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.

Acknowledgement

Grant : 병사들에게 실전과 같은 가상훈련 환경을 제공하기 위한 전 방향 이동 지원 상호작용 소프트웨어 기술 개발

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