碩士 / 國立虎尾科技大學 / 工業工程與管理研究所 / 94 / In Recent medical report, pancreas cancer has reached the 8th in leading cause of cancer death in Taiwan. Based on the epidemiology study, there is a high incidence of the pancreatic cancer in Euramerica, almost one out of ten thousands in morbidity. About 800 people die of this cancer each year in Taiwan, and the figure is up trending. Most of the prognosis for pancreas cancer is not specificity, such as in appetence, abdominal distension or pains are some demonstrations of characteristic clinical signs. Many clinicians treat patients with gastro duodenal or gallstone disease. It was not until the increase in pains or the pains extended to the dorsal that the tumor was found to invade the nearby organ, result in the lost of treatment in time.
When dealing with the abdominal disease, clinicians have to infer the related organ that causes the symptoms, physical examination and lab test were further taken. However, the characteristic of non specificity of the prognosis for pancreas cancer, clinical judgments vary with individual experience and perception or mental conditions usually causes the diagnosis bias.
This research applied Neural Network and Genetic Algorithm of Artificial Intelligence and Logistic Regression in Statistics to build three differential diagnostic models of Pancreatic Cancer and Acute Pancreatitis. The performance of all models was compared with Receiver Operating Characteristic (ROC) analysis. There are 234 cases for model trained and 117 cases for further testing. The results of the pairwise comparison shown that GALR model and SLR model had significant difference. However, the sensitivity and specificity of the GALR model is 96.7% and 82.5% which is better than SLR model of 96.7% and 73.7%, also BPN model of 88.3% and 84.2% at the optimal threshold for the three models. Which shows that GALR model has the better diagnostic performance. In the meantime, Artificial Intelligence will perform more accurate prediction when in larger and completed database along with advanced computing techniques.
| Identifer | oai:union.ndltd.org:TW/094NYPI5030002 |
| Date | January 2006 |
| Creators | MING-YUAN HSU, 徐銘遠 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 82 |
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