資料探勘主要之目的是在大量之資料中發現有用與有趣之模式。此技術近年來廣泛被應用於商業工業、醫學生物等領域。本文運用資料探勘中之多種貝氏分類法則，進行醫療數據集的預測，並以各分類器實驗來進行多項醫療數據集預測結果的比較研究。
貝氏分類法（Naïve Bayers Classifier）本身具有訓練執行效率快，且對複雜的醫療問題，可預測出良好精確度的優點。且其以機率為分類原則的特性，擁有深厚的統計理論基礎。本研究根據各個數據集，運用於不同的貝氏分類法則的預測結果，進行混沌矩陣、ROC曲線、正確率、精確度、召回率等進行績效評估，並應用ANOVA 單因子變異數分析，比較各分類器於不同數據集實驗結果。
本研究根據實驗結果，在不同的醫療數據集中，分析出各個數據集的分群，並依據實驗結果選出各項績效最好的組合進行分類。最後，希望能透過對醫療數據集比較分析，評斷出較好的貝氏分類方法。應用於不同醫療數據集之結果驗證與評估方式，了解何種醫療數據集在何種貝氏分類方法之預測結果較佳。

The purpose of data mining is that to figure out useful and logistic modules from a considerable number of data. In recent years, data mining is widely getting to be used in fields of commerce, industry, medicine biology and so on. This research is going to perform Naïve Bayers Classifier, one of the data mining methods, to predict the rules of medical data set and compare the prediction results of medical data set through sorts of analyzer experiment.
In terms of Naïve Bayers Classifier, there are a lot of advantages which are higher efficiency of training execution, precise prediction of complicated medical problems. Furthermore, the principle of classification is in accordance with probability which is based on statistics theory. In this research, it is going to conduct different Naïve Bayers Classifier to predict results from each of data set and moreover to evaluate effects through Chaos Matrix、ROC curves、accuracy、precision、recall and so on. Finally, it is going to compare classifier in results of different data set by adopting One-way ANOVA analysis.
According to the results of experiments, in different medical data set, it could be analysed and arranged each data set and select the best association in a basis of evaluation. In conclusion, through analysis of medical data set it could be evaluated better methods from Naïve Bayers Classifier and used in verification of different medical data set and evaluation to indicate what kind of Naïve Bayers Classifier will obtain better predict results.

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