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URN etd-0301106-222121 Statistics This thesis had been viewed 1924 times. Download 24 times. Author Rui-yi Lin Author's Email Address email@example.com Department Information Management Year 2005 Semester 1 Degree Master Type of Document Master's Thesis Language English Page Count 72 Title Applying Data Mining Technology on National Health Insurance Research Database-For Example:
Chronic Renal Failure (CRF)
Keyword health insurance research database decision tree data mining chronic renal failure association rule association rule chronic renal failure data mining decision tree health insurance research database Abstract Because of the advance of food, life, and medicine, people live longer and longer. Over 40,000 patients with chronic renal failure (uremia) accept the treatment of health insurance. According to the statistical data of Department of Health, Executive Yuan, kidney disease is ranked eighth place among ten major causes of death in Taiwan. And the most of the kidney diseases are chronic renal failure.
In this article, we use the health insurance research database during 1997 to 2000 as the source and pick up Ambulatory care expenditures and the registry for contracted medical facilities to analyze those people who take uremia which international disease code encoded as “585”. After the analysis of basic statistics, it shows that: 1. Male is more dangerous than female and most of the patients are about 60~79 year’s old. 2. Most of the patients live in the Hsinchu City, Tainan city is next. 3. Patients with kidney disease are used to taking medical treatment on National Medical Center and registering mainly at Department of Hemodialysis, secondly Department of Nephrology.
In addition, we also concern about the relations of uremia with other diseases and whether the medical institutes giving inflated expenses. The data mining technologies are adopted the association between diseases by association rules and to induce the conditions of expenses between declaration and charging of all medical institutes by decision tree.
We pick up the useful information by the technologies of data mining, in order to provide not only the meaningful medical knowledge for doctors’ reference, but come into government and social publics’ notice and alarm people about the diseases protections . On the other hand, help the health insurance bureau to detect unusual cases and fine the false cases and intent to decrease the unnecessary cost and wastes. Hope this research is a contribution to human healthy and aids the government making projects in kidney protection.
Advisor Committee Yen-ju Yang - advisor
none - co-chair
none - co-chair
Files Date of Defense 2006-01-17 Date of Submission 2006-03-01