腦血管疾病一直是造成國人的主要死因之一，而近年來，儘管醫療技術的進步，但隨著國人的生活、飲食習慣的改變，使得患有腦血管疾病之患者不減反增，名次逐年上升，不僅單單造成醫療上的支出增加，加上醫療照顧的支出以及社會成本更是龐大，唯有及早發現並進行預防，才有可能減少腦血管疾病對個社會國家的負擔。
　　發現是否有腦血管疾病較為有效的方法是進行頸部顯影檢測，但在一般健檢項目中並不包含此項檢查，且進行此項檢查不僅需要檢測時間，更需額外支出一筆費用，降低民眾檢測之意願，以致錯失及時治療和預防的契機。
　　因此，本研究與北部某醫學中心合作，使用其提供之健康檢查資料進行資料探勘，包含結合基因演算法、K-Means分群法以及Apriori關聯規則演算法，期望能從一般健康檢查的項目中，找出與頸動脈病變相關性較高之重要因子。使患者能透過一般健檢項目得知自己是否為腦血管疾病的潛在患者，也能協助醫生進行判斷病患的狀況，以預防勝於治療的概念，來降低腦中風的發生，進而減少龐大的醫療以及社會成本之支出。

　　Cerebrovascular disease has been one of the main causes of death of people. In recent years, with the people’s living and eating habits changing, although the medical technology advances, patients with cerebrovascular disease have been increasing rather than decreasing. The ranking is growing gradually. The only way is to find out early and prevent it so that it is possible to reduce the burden of cerebrovascular disease in a country.
　　The best way to find out the cerebrovascular disease is cerebrovascular ultrasound detection, but this is not included in the general health examination. Thus, most of people will ignore it and miss the opportunity of treatment
　　This research got the health examination database from cooperation of medical center in Taipei. We want to use the techniques of data mining to find some factors and relation between health examination database and Carotid disease.
　　Finally, we hope we can use the factors from health examination database to find out the potential patients of Carotid disease.

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