近年來，腦血管疾病在國人十大死因的排名居高不下，儘管醫療技術的進步，但隨著國人生活和飲食習慣的改變，使得國人罹患慢性病的人數有增無減，不僅需要龐大的醫療支出，更是造成殘疾人數增加的主因之一。
雖然國內健保體制健全，鼓勵民眾定期健康檢查，但是要了解血管粥狀硬化的情況尚須透過頸動脈超音波或核磁共振，而目前國內的頸部顯影檢測皆需要額外支付費用，這將會降低民眾更進一步檢測的意願，以致錯失及時治療和預防的契機。
　　本研究將與北部某醫學中心合作，取得腦部健檢資料，以資料探勘的方法對資料進行分群、離散化、搜尋關聯法則，從一般健康檢查的項目中，找出與頸動脈病變高度關聯性的屬性區間值，所找出的關聯法則lift(代表正相關)大於1.17且信賴度均高於60%，藉由找出的關聯法則建置頸動脈臨界值預警系統，協助醫生能透過一般健檢資料去分析健檢者是否為罹患頸動脈病變的高危險族群，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。

Cerebrovascular diseases, or strokes, have always been highlighted as a big threat to health in Taiwan as well as worldwide. It is costly to detect stroke through brain image examination like Magnetic Resonance Imaging (MRI). Data mining has been used widely to analyze different types including medical data. In this research, data mining is used to find the association rules for stroke. The purpose of this study is to generate high confidence of association rules from patient’s cerebrovascular examination results as a reference.
In this research, a dataset of cerebrovascular health examination from a local medical center in Taiwan is used. Three kinds of clustering approch will be applied first. They are Particle Swarm K-means Optimization (PSKO); Self-Organizing Map Neural Network (SOM) and Density-based Spatial Clustering of Applications with Noise (DBSCAN). In the next stage, K-means approach will be used to discritize the discrete features in the dataset. Finally, the association rules from each discritized cluter will be identified by applying Apriori method. Hence, based on these finding rules, the physicians can pay more attention and aware to the features highlighted. This research has focused on the association rules mining based on discretization method, to identify the important features to be concerned behind the cerebrovascular disease, as well as considered common pattern of each cluster.

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