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Title page for etd-0209112-001343


URN etd-0209112-001343 Statistics This thesis had been viewed 3535 times. Download 3973 times.
Author Wei-lung Chien
Author's Email Address No Public.
Department Information Management
Year 2011 Semester 1
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 66
Title Prediction of Falling Risk of the Elderly by Data Mining Techniques
Keyword
  • Falls in the elderly
  • Classification analysis
  • Feature selection
  • Data mining
  • Risk Prediction
  • Regression Analysis
  • Regression Analysis
  • Risk Prediction
  • Data mining
  • Feature selection
  • Classification analysis
  • Falls in the elderly
  • Abstract A matched case-control (case:control=1:2) study was performed among nursing home residents of 65 years and older during 1-year period. The victims were interviewed with in-charge nurse by questionnaire after the event as soon as possible. Another matched (age±5 year, sex, neighborhood) control group was visited at the same time. All the falls-associated factors such as medication experience, underlying disease, surroundings and the motion accompanied the falls were included as independent variables and the dependent variable was the fall attack.
    In this research, the statistical method of Mantel-Haenszel Test is adopted for feature selection and significant variables are preserved. Then 2 data mining techniques, logistic regression and decision tree are applied to construct classification model and the accuracy is compared among intra- variant models.
    Instability during the action before the fall is the important factor. The actions including rising from bed or toilet, standing on the chair and taking something from the above-head place, etc. The accuracy of 2 classifiers is all above 80%. The good results can be used to predict falls in the elderly and give reference for the elderly care personnel.
    Advisor Committee
  • Yen-ju Yang - advisor
  • Sung-chien Lin - co-chair
  • Yu-cheng Kao - co-chair
  • Files indicate access worldwide
    Date of Defense 2012-01-17 Date of Submission 2012-02-09


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