近年來，腦血管疾病在國人十大死因中的排名不見減少，反而有逐年上升的趨勢，這是在醫療技術與生活水準漸漸提高，定期做健康檢查的人數上升的情況下仍需要密切關心的情況。
這種情況產生的原因有部份是由於一般健康檢查所帶來的資訊較為不足，若要明確的判斷是有否罹患腦血管疾病的風險，則需進行頸部顯影檢測，這部分檢測需要支付額外的醫療費用，降低民眾檢測的意願，有許多民眾因此錯過了及早發現的可能性，造成額外更高的醫療支出，而在腦血管疾病當中，中風是最具代表性，造成的醫療支出也是最高的。
故本研究與北部某醫學中心合作，使用其提供之腦部健康檢查資料與一般健檢資料，以文獻的探討與資料探勘的方法尋找一般健檢資料擁有的屬性與中風之間的關聯性，並以相關的屬性組合成中風的規則，藉由可接受的準確度與支持度輔助，使規則具代表性。
由於規則是以一般健檢資料中的屬性所組成，故醫生可結合規則與自身專業知識，提供病人合適的建議與需要注意的地方。此外，亦可探討規則與規則之間的異同之處與其所具有的醫療上的意義。

Stroke has always been highlighted as a big threat of health in the worldwide.
Magnetic Resonance Imaging (MRI) and ultrasound are few tools can be used to discover stroke disease. However, it required extra resources (both in time and cost) for the doctors and patients to carry out the exam.
On the other hand, taking general health exam is a popular activity for the people in Taiwan. Therefore, in this research, a hybrid data mining approach applying general health exam data to extract the appropriate decision rules is proposed. It can be used to support medical personnel to make appropriate suggestion for brain image examination.
This research used a brain health exam dataset collected by a medical institute in north Taiwan from 2004 to 2011. This dataset includes general healthcare checkup and MRI brain exam. There are three stages in this approach. The stratified random sampling approach will be applied to construct a balanced health exam record dataset for the patients with stroke history. Then, genetic algorithm will be used to identify the most related factors for stroke from the health exam items. Finally, data for the identified items will be used to construct the decision rules based on C4.5 approach. The quality of the constructed rules will be examined by their precision and support. These decision rules may used to assist doctor to make appropriate judgments for the patients with stroke possibilities.

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