臺灣博碩士論文加值系統

在醫療領域中，資料缺失是時常可見，而遺漏值的存在，將會影響到醫生學者們對該資料集的分析及預測結果。現今醫療研究中，大多數研究都專注在最佳正確率模型建立上，並沒有考慮模型在不同遺漏比率、遺漏型態下的穩定度。綜上所述，本研究提出一新穎的插補方法來處理資料不完整與穩定性問題。基於資料完整性與易用性，本研究提出以距離為基礎的最鄰近區插補方法，來提升遺漏值插補上的穩定性與資料完整性。在公開資料集實驗中，本論文選用了多個不同資料維度的UCI公開資料集，並製作出不同遺漏比率與遺漏型態資料集用於實驗，並將實驗正確率結果與常見的插補技術做比較。在實際資料集實驗，利用來自全國中風調查(IST)的中風資料集驗證所提出的方法是否能有效運用在實務方面。從結果來看，本研究提出的方法能夠在不同的遺漏比率、遺漏型態、資料集的模擬實驗下，取得良好成績。此外也能在中風資料集獲得高達百分之九十的正確率。這代表此方法能夠有效應用於實務資料集當中。

In medical filed, missing data is often existed, which will affect the analysis and prediction by doctors and scholars. Most studies have focused on the highest accuracy prediction model in current medical research. However, they do not have considered the stability of models with different missing degrees and different missing types. Based on data complete and easy operation, this study proposes the imputation method, which is nearest neighbors method based on distance to imputation missing value. In the experiment, this study selected several UCI datasets and produced different missing degrees and missing types. Comparing training accuracy with popular imputation methods. Moreover, using the Stroke dataset from the International Stroke Trial (IST) to verify whether the proposed method could be effectively used in practice. The results show that, the proposed method can have good performances under different simulations of missing degrees, missing types, and datasets. In addition, the proposed method can obtained 90-percentage accuracy in the Stoke dataset. It means the proposed method can be effectively used in the practice dataset.

摘要....................................i
Abstract................................ii
Content.................................iii
List of Tables..........................iv
List of Figures.........................v
1. Introduction....................1
1.1 Background......................1
1.2 Motivation and objectives.......2
1.3 Organization of the thesis......2
2. Related Work....................4
2.1 Missing value in Healthcare.....4
2.2 Nearest Neighbor Technique......5
2.3 Type of missing values..........7
2.4 Imputation methods..............8
3. Proposed Method.................12
3.1 Concept.........................12
3.2 Proposed algorithm..............14
Step 1. Data Collection.................15
Step 2. Data preprocessing..............15
Step 3. Data imputation.................17
Step 4. Classification and Evaluation...18
4. Experiment and Results..........20
4.1 Experimental environment........20
4.2 UCI Datasets Experimental.......20
4.3 Stroke Datasets Experimental....36
4.4 Finding.........................39
5. Conclusion......................42
References..............................43

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