| Budget Amount *help |
¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1999: ¥800,000 (Direct Cost: ¥800,000)
|
|---|
| Research Abstract |
We have tried to adopt the several nonparametric data analysis methods, such as GAM, MARS, decision tree, MART, hierarchical artificial neural networks (HANN), and Livingstone type artificial neural networks (LANN), to the data in the field of medical and pharmaceutical sciences. And the reasonable results of regression and principal component analyses were obtained.
First, we compared the two nonparametric nonlinear regression methods, MARS and MART, which were developed by Freedman et al., using ideal artificial data. MARS showed a surprisingly good fitting and prediction performances ; MART also showed quite good performances. And the results indicate that MART is a robust data mining method. Thus, we resulted that the MARS should be adopted for the data containing little noise and that the MART should be adopted for the data analyses which can be affected by noise.
Then, we applied these methods including the GAM and the HANN to some epidemiological data sets. For example, the MARS a
… More
nd the MART methods were applied to the pharmacoepidemiological study of estrogen, which might be related to the onset of endometrial cancer. The results show that the active form of estrogen is related to the onset of the cancer. Although we could get a certain information of the pharmacoepidemiological study of estrogen, it was not so easy to test the significance of each prediction variables used in such nonparametric models. Thus, we introduced the extended shift test method, which was revised by our group, in order to test the significance of prediction variables. These methods were also applied to the clinical epidemiological study on the relation between onset of esophagus cancer and alcohol consumption. We confirmed the possibility that the alcohol consumption affect the esophagus cancer.
In addition, we applied the LANN to the nonlinear principal component analyses of Gas-Liquid chromatographic retention data. The clearer classified result than the one obtained by linear PCA method could be obtained. Less
|
