隨著國內醫療技術及知識水準的提升，國人逐漸意識到定期健康檢查的重要，但因生活環境和飲食習慣的改變，使得國人患有慢性病的人數逐年增加，其中，腦血管疾病在國人十大死因的排名高居不下，不僅造成龐大的醫療支出，更是殘疾人數增加的主因之一。而每年約五萬名腦血管疾病患者中，大約有一萬名為頸動脈狹窄導致的缺血性中風，所以，定期檢測頸動脈以預防病變，可以有效降低腦中風的可能性。然而，目前國內的頸部顯影檢測皆需要額外支付費用，大幅降低民眾檢測的意願。
　　因此，本研究將與北部某醫學中心合作，取得腦部健康檢查資料，以資料探勘的方法結合啟發式演算法應用於其中的頸動脈診斷結果，篩選出提升預測頸動脈病變準確率的重要因子並建置分類預測模型，而模型的訓練準確為80.1%，測試準確率為82.1%。
　　然而，單純的分類預測能協助醫生的資訊並不多，因此，本研究最後將再建置案例式推理規則，讓醫生能透過一般健檢資料去分析和判斷健檢者頸動脈病變的可能性，使罹患機率高者，能盡早預防及治療，以減少大量的醫療支出及提升國人的生活品質。

　　With the medical technology and people knowledge have been promoted, people are aware that heath examination is important. However, living environment and dietary habit gradually change. Lead to number of patients with chronic illnesses is rising. In which, cerebrovascular disease is the top ten leading causes of death and one of the main reason for the increase in the number of people with disabilities.
　　This study got a brain health examination database from cooperation of medical center. Hope that through data mining techniques and heuristic algorithm apply in prediction of carotid diagnostic. Applications include feature selection and prediction model construction, predictive accuracy of model for training is 81.1% and for testing is 82.1%.
　　However, the simple prediction result is not enough for what assist doctors. Therefore, this study constructs case-based reasoning rules to assist doctors what obtain information more. Doctors can through health examination report to analyze and predict carotid diagnostic result for patients.

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