遺漏值填補(Missing Value Imputation, MVI)是研究人員進行資料分析的重要過程，因為大多數的機器學習方法都不適用於不完整的數據集(如神經網絡和支持向量機)，隨意忽略該步驟更會導致嚴重的分類錯誤。對於醫療領域來說，因為並非所有可能的測試都可以對每個患者進行，再加上人為疏失、設備故障等意外因素干擾，遺漏值的存在已是一個常見的問題，這不僅增加了相關人員在分析、預測等任務上的難度，也影響了患者所應該受到的即時診斷和治療。
最後的研究結果顯示，由本研究所提出的RFE_missForest分別在3種連續型數據集以及3種混合型數據集上，不論是NRMSE或是PFC都有著最好的表現，優於其他4種現有的補值方法，並且統計差異顯著。 ;Missing Value Imputation (MVI) is an important process in data mining, because sometimes it will cause serious problems for classification. One of the most serious problems is that the majority of classification algorithms do not work on incomplete datasets (such as neural networks and support vector machines). In the medical field, because of not all possible tests can be done on every patient, and coupled with the interference of accidental factors such as human negligence and equipment failure, the existence of missing values is a common problem. It not only increases the difficulty in tasks such as analysis and prediction, but also affects the immediate diagnosis and treatment that patients should receive. In the research field of missing value imputation, missForest is a very popular imputation method. Although its performance has been proved to be better than other known imputation methods, there are few studies considering its optimization or further discussion. Therefore, this study tried the feature selection method currently popular in missing value imputation research—RFE, combined it with missForest and propose a new imputation method RFE_missForest. We used a total of 10 medical data sets obtained from Kaggle and UCI, simulating the missing rate of 10% to 50%, then compare the filling quality of continuous and categorical data sets with missForest and three other traditional imputation methods. Experimental results show that our RFE_missForest algorithm has the best performance both on 3 continuous data sets and 3 mixed data sets, whether it is NRMSE or PFC. The proposed method was also validated by t-test and has a significant difference.
