應用Data Mining建置一分類模型

Applying Data Mining to Build a Classify Model

10.29767/ECS.200703.0006

戴建耘(Chien-Yun Dai)；盧治均(Chih-Chun Lu)；廖秋惠(Chiu-Huin Liao)

资料挖掘 ； 资料倉儲 ； 决策樹 ； Data Mining ； Data Warehouse ； Decision-Tree

Electronic Commerce Studies

5卷1期（2007 / 03 / 31）

109 - 123

繁體中文

本研究以资料挖掘(Data Mining)、资料倉儲(Data Warehouse)以及分類(Classification)演算法规则中之决策樹(Decision-Tree)爲基礎之分類法做爲本研究之工具，以心臓科麻醉部爲例，從醫療單位對抗生素進行導管菌落判断感染监控的歷史案例中，建構出可用以预测心静脈導管塗抹抗生素在病患身上的作用結果之分類模式，用以協助醫藥人員有效判断感染情形與特徵，而提升抗生素之用藥醫療品質、降低可能的醫療资源浪费。 實驗結果顯示，本研究所收集的導管菌落判断感染監控资料中有顯著類别間资料數量不對稱的现象，而造成模式预测偏向的問題。經由成本敏感法的連用预测錯誤成本的改善绸整，提升分類预测的效能，建構出對各類别资料都有良好预测效能的理想模式。目的做爲日後舆專家系統之結合，發展出一套强而有力之醫療專家系統，减少醫療费用的浪费，同時在照顧病人品質方面能夠维持一定的水凖。

The research use Data Mining, Data Warehouse and Decision-Tree that Classification perform the algorithm makes for the tool. Take department that anaesthetizes division of cardiology as an example. Build and construct out measurable Central Venous Catheter to apply in the historical case of judging Central Venous Catheter Insertion from the antibiotic and infecting Wipe the categorized model on patient of antibiotic. The paper use under-sampling, over-sampling and Part to solve skewed class distribution problem. And then use C4.5 algorithms to build up classifier. Verify via the expert that use this classifier can effectively help the medical expert to extract out it about Central Venous Catheter Insertion good knowledge rule.

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