Earticle

초록

영어

There are many problems with applying the machine learning technique, which is widely used in the conventional healthcare field, during the mobile u-health service analysis step. First, research on the mobile u-health service is just beginning, and there are very few cases where the existing techniques have been applied in the mobile u-health service environment. Second, since the machine learning technique requires a long learning period, it is not suitable for application in the mobile u-health service environment, which requires real-time disease management. Third, the various machine learning techniques that have been proposed until now do not include a way to assign the weight factors to the disease-related variables, and thus its use as a personalized disease prediction system is somewhat limited.
This paper proposes PCADP, which is an ontology-based personalized disease prediction method, to solve such problems and to interpret the bio data analysis of the mobile u-health service system as a process. Moreover, the mobile u-health service ontology framework was modeled as a semantics type in order to meaningfully express the mobile u-health data and service statement based on PCADP.
To validate the performance and efficiency of the PCADP technique proposed in this paper, the 5-cross validation method was used to measure the accuracy of the prediction. The validation of PCADP using a virtual disease group verified that the technique proposed in this paper shows much greater accuracy compared to existing methods. Moreover, the PCADP prediction method improved the flexibility and real-time attributes, which are the essential elements of any diagnosis technique in the mobile u-health environment, and showed efficiency in the continuous improvement of the monitoring and system of the diagnosis process.

목차

Abstract
 1. Introduction
 2. Mobile U-health Service Personalized Disease Prediction Method
  2.1. Disease Diagnosis Algorithm Architecture
  2.2. Learning Stage
  2.3. Decision Tree Stage
  2.4. Prediction Stage
  2.5 Feedback Stage
 3. Mobile U-Health Service System
  3.1. Definition
  3.2. Elements of the Mobile U-health Service
  3.3. Mobile U-health Service Platform
  3.4. Mobile U-health Service Scenario
 4. Implementation of the Mobile U-Health Service System
  4.1. Data Used for Validation
  4.2. Validation
  4.3. Validation Test Result
  4.4. Comparison and Consideration
 5. Concluding Remarks
 References

키워드

저자

• Byung-Won Min [ Department of Information Communication Engineering, Mokwon University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605