Applying Integrated Data Mining Techniques to Assist Diagnosis of Liver Disease
| Autor: | Chi-You Liu, 留啟祐 |
|---|---|
| Rok vydání: | 2008 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 96 Liver disease is the most common local disease in Taiwan. According to the statistics from Department of Health in 2007, around ten thousand people die from liver cirrhosis, liver cancer and other liver diseases because the symptoms of liver disease are not obvious in the initial stage, and the condition is usually too serious to be treated when related symptoms make themselves felt. Developing an assisted liver disease diagnosis model has therefore become a major issue attracting growing attention from scholars and researchers. This study accordingly aims at constructing an optimal integrated liver disease diagnosis model (ILDM) by collecting patient data, using data mining techniques, integrating expert opinions, and utilizing genetic algorithm that is capable of finding best combination of diagnosis models. Moreover, MARS (multivariate adaptive regression splines) is adopted to obtain significant diagnosis variables, helping to construct a more efficient diagnosis system. As the results reveal, the integrated data mining techniques of diagnosis model outperforms the single data mining techniques of diagnosis model. Using GA helps reduce the time and cost spent on model construction and speed up the identification of the best combination of ILDM. In addition, the diagnosis model established by MARS outperforms the diagnosis model with no screening variables. ILDM can be expected to decrease the possibility of delays in medical treatment caused by wrong diagnosis and save medical costs by eliminating unnecessary inspections. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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