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研究生:傅鈞懋
研究生(外文):Jiun-mau Fu
論文名稱:實證醫學概念應用於腦中風之預測模型
論文名稱(外文):The prediction model of brain stroke using EBM
指導教授:顏逸楓
指導教授(外文):Ester Yen
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊管理所暨醫療資訊管理所
學門:教育學門
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:48
中文關鍵詞:負關聯規則腦中風序列型樣實證醫學
外文關鍵詞:Negative Association RuleBrain StrokeEBMSequential Pattern
相關次數:
  • 被引用被引用:0
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  • 下載下載:47
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腦中風通常是因腦血管破裂或阻塞,導致腦部的供血量不足而造成腦細胞受損所致。本篇研究基於負關聯規則、序列型樣以及事證醫學的概念,進而提出了一個腦中風預測系統,此系統可用來預測某人是否可能患有腦中風。透過跨時空與跨空間來蒐集文獻並整理出所有腦中風的危險因子,再將這些因子資料用於我們資料探勘中。所提出的模型可用於腦神經科及相關疾病的預測與診斷,並提供於醫事人員作為制定決策時的參考依據。經過實驗後發現,本預測模型提供了良好的準確度。
Stroke, also called brain attack, caused by the interruption of the blood supply to
the brain, usually because a blood vessel bursts or is blocked by a clot. This cuts off
the supply of oxygen and nutrients, causing damage to the brain tissue. This paper
proposed a prediction model of brain stroke based on negative association analysis,
sequential pattern analysis and EBM concept, for predicting whether a person will
suffer from brain stroke. We summarize several studies of stroke for identifying the
features used for our mining. Based on the proposed model, the result of prediction
could be applied to Neurology or other related diagnosis, and then the results also
could be provided as important reference to help physicians in decision making. After
several experiments were conducted, the results demonstrated that the proposed
model provided high accuracy and could be used as a prediction system.
1 Introduction ............................................................................................................... 1
2 Related Work ............................................................................................................ 4
2.1 Brain Stroke .................................................................................................................. 4
2.2 Evidence-Based Medicine ........................................................................................... 5
2.3 Data Mining .................................................................................................................. 8
3 Materials and Methods ........................................................................................... 13
3.1 Data Preprocessing ..................................................................................................... 14
3.2 Kernel Methods .......................................................................................................... 19
3.3 Model Construction ................................................................................................... 23
4 Evaluation Method.................................................................................................. 25
4.1 Confusion Matrix ....................................................................................................... 25
4.2 ROC Curve .................................................................................................................. 27
4.3 Results and Discussions ............................................................................................ 28
5 Conclusion ............................................................................................................... 33
5.1 Limitations and Challenges ....................................................................................... 33
5.2 Research Contributions and Future works .............................................................. 34
Reference .................................................................................................................... 35
Appendix ..................................................................................................................... 41
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