| Summary: | 碩士 === 朝陽科技大學 === 工業工程與管理系碩士班 === 101 === In recent years, people more and more pay attention to the health examination. The purpose of health examination aimed is to detect disease in the early stage. Screening by health examination , some diseases may be found early and receive proper treatment. From literature, it can be found that the mortality of cancer has increased in recent years. The mortality of colorectal cancer has in the first place among all other cancer diseases and cannot be ignored.
In this study, the health examination data from Yi-Siang Lin (2011) is applied to construct prediction model for colon abnormalities by using the neural network method. Cross validation of different K-fold are applied in the neural network models; different parameter settings are used to assess the performance of the predicted models; different imbalance sample data are used to assess the performance of the performance of the models; the impact of clustering the highly correlated health examination items to the model prediction are also evaluated.
In the study of imbalanced sample data, when the ratio of the observed data of normality/abnormality from the colonoscopy in the training sample is higher than that in the testing sample, it resulted in a lower accuracy of prediction. When the highly correlated health examination items are clustered and colonoscopy results are divided into two categories (normality/abnormailty), the accuracy of 5-fold cross-validation is better than 10-fold cross-validation. Finally, when the highly correlated health examination items are clustered, the accuracy of the model (63.98%) is better that of the model with no clustering (59.68%).
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