| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 98 === Abstract
Malignant neoplasm (cancer) tops the ten leading causes of death in Taiwan, followed by heart diseases and cerebral-vascular diseases. In the past, limited to and confined in the studies of the impact of carotid artery intima-medial thickness on atherosclerosis. Few studies were conducted focusing on whether the common carotid artery diameter (CCAD) would increase chances to develop cerebral-vascular diseases. It is crucial to develop a set of diagnostic system that can help doctors in clinical diagnosis and prevention.
In this study, we mainly discussed the degree of influence of the inner layers of the blood vessels and the relative various factors associated with the blood vessel diameters. We conducted analysis on the CCAD and the CIMT of the healthy people, used the artificial neural network and decision tree of the data mining technology to perform analysis objectively on the database of the case hospital, and evaluated the differences of the effective clinical diagnostic indicators of atherosclerosis, CCA-IMT and CC-AD.
The study results indicate that accuracy of artificial neural network in CCA-IMT classification prediction is 82.19%, while that of CC-AD is at 82.36%. Regarding the decision tree model, the classification accuracy is 97.28%, while the classification results using artificial neural networks show that the major factor with the major influence is age. Simultaneously, it can help thorough planning for early prevention strategies to reduce possibility of developing atherosclerosis, thus bringing practical advantages to clinical diagnosis.
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