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，進而累積了豐富的健檢資料，若是正確地運用這些資料，找出具參考價值的資訊，對於醫院規劃醫療策略時非常有幫助。
目前醫院提供的健康檢查報告中，醫生建議的項目通常只會顯示健檢者的異常項目，所以健檢者往往因不了解異常項目之可能變化而不一定會回檢。而醫院方面也較少探討再次健診民眾其檢查項目之變化情況，結果健診民眾可能錯失治療和預防的契機，而醫院方面也無法針對其現有健診客戶提出要求再次回診之適當原因。因此，本研究將與北部某醫學中心合作，運用腦部與一般健檢資料藉由關聯資料探勘的方法建立ㄧ回診民眾健檢屬性變化之關聯法則模式以提供醫院管理者參考。
由於傳統之關聯法則搜尋法如Apriori法是依信賴度及支持度去進行法則搜尋，有可能找出在項目集與法則數上無法滿足使用者(如健檢管理單位)之一組關聯法則(低項目集數/較大量之關連法則數；或是高項目集數/較少之關連法則數)。本研究提出一巢狀基因演算法，依使用者定義之參數如最少項目集數，最多關連法則數等，找出滿足所設定多目標之關聯法則。該方法分別是先以外層基因演算法進行健檢參數屬性離散化，接著以內層基因演算法進行健檢民眾回診資料關聯規則之搜尋，找出滿足多目標之一組關聯法則。最後再根據關聯法則建立健檢者前後健檢項目數值的樣板模式。醫療機構則可藉由此模式來評估健檢者未來健檢參數之變化，並進而對個別健檢提出是否需回檢之建議。本研究在醫院管理的應用上不僅達到即早治療及預防的目的，還能提升醫療服務品質、提高健檢回檢率。

With the upgrade of domestic medical technology and level of knowledge, people gradually realize the important of periodic health examination and thus accumulated a lot of health check-up data. These data actually can be very useful for hospital planning health policy if we use it correctly.
In the health examination report of the hospital, physicians often give some advice which is about abnormal items of examinees. However, the examinees generally do not understand what that exactly meaning, and they may not to come back to do another check-up. Furthermore, on the aspect of the hospital, they seldom discuss the situations of health Examinees re-coming, and can not request examinees to do another check-up without appropriate reasons. As a result, the examinees may miss the opportunities of therapy and prevention. Therefore, a dataset of cerebrovascular health examination from a local medical center in Taiwan is used in this research. we use data mining method and association rules mining to establish the patterns of the change of the examinees’ health examination attributes, and physician can take it as the reference.
The traditional association rules mining like Apriori use support and confidence to find association rules, the number of itemset and rules which may not fulfill users’ want. In this research, we design a nested genetic algorithm, which can provide users to define their own parameters, like minimal itemset and the maximal number of rules, to find association rules. First, we use outer GA to discretize the continuous features, then use inner GA to find the association rules of examinees’ health examination. Finally, we use the association rules to establish the patterns of health Examinees' re-coming. According to the results, physicians can use it to evaluate examinees’ future changes of health, and suggest the examinees need another check-up or not. In this research, we not only reach the purpose of early treatment and prevention but also enhance the quality of medical service and the health check-up rates.

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