Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Symptom Pattern Classification using Neural Networks in the Ubiquitous Healthcare Environment with Missing Values

손실 값을 갖는 유비쿼터스 헬스케어 환경에서 신경망을 이용한 에이전트 기반 증상 패턴 분류

  • Received : 2009.06.22
  • Accepted : 2009.12.02
  • Published : 2010.04.30
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Abstract

The ubiquitous healthcare environment is one of the systems that benefit from wireless sensor network. But one of the challenges with wireless sensor network is its high loss rates when transmitting data. Data from the biosensors may not reach the base stations which can result in missing values. This paper proposes the Health Monitor Agent (HMA) to gather data from the base stations, predict missing values, classify symptom patterns into medical conditions, and take appropriate action in case of emergency. This agent is applied in the Ubiquitous Healthcare Environment and uses data from the biosensors and from the patient’s medical history as symptom patterns to recognize medical conditions. In the event of missing data, the HMA uses a predictive algorithm to fill missing values in the symptom patterns before classification. Simulation results show that the predictive algorithm using the HMA makes classification of the symptom patterns more accurate than other methods.

무선선서네트워크의 주요 응용분야 중 하나가 유비쿼터스 헬스케어 시스템이다. 하지만 무선센서네트워크가 가지고 있는 과제중의 하나는 데이터 중에 나타나는 높은 손실 율이다. 바이오 센서로부터 들어오는 데이터는 기지국에 도착되지 않을 수 있으며, 이 값은 손실 값(missing value)이 된다. 본 논문은 기지국에서 데이터를 수집하고, 손실 값을 처리한 후, 증상 패턴에 따라 건강상태를 분류하여, 비상시에 적절한 행동을 취할 수 있도록 하는 헬스케어 모니터 에이전트(HMA)를 제안한다. 이 에이전트는 유비쿼터스 헬스케어 환경에 적용되며, 건강상태를 인지하기 위한 증상패턴으로 바이오 센서 및 환자의 가족력으로 부터 생성된 데이터를 사용한다. 손실 값이 나타나면 HMA는 분류하기 전에 증상패턴의 손실 값을 채우기 위한 예측 알고리즘을 수행한다. 시뮬레이션 결과 HMA를 사용한 예측알고리즘이 다른 방법들에 비해 더 정확하게 증상패턴을 분류함을 보여주었다.

Keywords

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