研究生: |
余姿瑱 TZU-TIEN YU |
---|---|
論文名稱: |
發展以亂度為基之監督式離散化方法於腦中風稀少關聯法則探勘 Developing Entropy-Based Discretization Method to Discover the Rare Association Rules of Cerebrovascular Disease |
指導教授: |
歐陽超
Chao Ou-Yang |
口試委員: |
郭人介
Ren-Jieh Kuo 汪漢澄 Han-Cheng Wang |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業管理系 Department of Industrial Management |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 88 |
中文關鍵詞: | 腦中風 、亂度 、稀少關聯法則 、Apriori-Rare |
外文關鍵詞: | Cerebrovascular Disease, Entropy, Apriori –Rare, Rare association rules |
相關次數: | 點閱:413 下載:4 |
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腦中風是國人所熟知的高風險疾病,雖然由腦部核磁共振(MRI, Magnetic Resonance Imaging)檢查可了解腦部血管阻塞之狀況,但由於腦部MRI並非一般健檢項目,因此民眾除非安排專門檢查,一般來說都無法得知自己腦部血管阻塞的程度。然而一般性的健康檢查則是國人可以定期安排的檢查,因此本研究擬運用腦部健檢資料集(包含一般性健檢資料與MRI檢查結果之診斷資料)去探勘一般健檢項目與腦中風的相關性。
由於Apriori關聯分析法運用高頻項目集之概念去找出滿足最小支持度(Minimum Support)與最小信賴度(Minimum Confidence)的關聯法則,因此往往探勘出的是一些引起中風的已知法則(例如:高齡、高血壓、高血脂、高膽固醇等)。但實務上仍有不少腦部血管病變患者只具有部分特徵(例如:中齡、BMI正常、高血脂、高膽固醇) ,但往往因其發生頻率不是很高而被忽略。而具有這些特徵的民眾,將來可能也有發生腦中風的風險。 所以本研究擬藉由探勘一般健檢項目中,支持度相對次高的項目集中所得的知識進而協助健檢醫師對腦中風的預防。
本研究擬先運用亂度找出雜亂程度最低的區間組合,以利於後續關聯法則的結果。而探勘這些尚未廣為人知之知識項目集的支持度往往比已知知識項目集支持度,因此使用Apriori -Rare演算法,找出支持度相對較低的項目及,挖掘出本研究目標的腦中風未察覺項目法則,找出一些可能引起腦中風的次要屬性區間之關聯預測模型。
Brain 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). Therefore, most of the people don’t know their cerebrovascular conditions.
Apriori is a well known association mining methodology. It can identify the high frequencies itemsets fulfilled the required minimum support and confidence. However, when applying this method in cerebrovascular disease mining, the identified item sets are usually well known due to the concept of high frequency mining. However, in the real world, many patients with cerebrovascular disease might have few rare known symptoms such as medium BMI and medium ages. Therefore, this research will propose an entropy based discritization method along with a Apriori-Rare method to identify the association rules with those rare known knowledge.
This approach includes two stages. In the feature discretization stage, entropy is used as an index to identify the feature intervals with low uncertainty. Then the identified intervals will be applied by the Apriori-Rare algorithm to find the association rules with rare known symptoms. These rules can be a reference for doctors to identify the potential cerebrovascular disease patients.
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