臺灣博碩士論文加值系統

所謂試管嬰兒(in vitro fertilization, IVF)就是體外受精及胚胎移植的俗稱；將卵子和精子取出，在體外（試管或培養皿）受精，並在培養箱3～5天分裂發育成胚胎，然後在將胚胎植入回母體內，讓胚胎在子宮內著床懷孕。隨著現今的醫療體系進步與發展，但試管嬰兒的治療仍是複雜且難以預測出成功率。本研究的目的在於應用大數據分析(Big Data)技術在IVF資料庫中分析，並建構出結果預測模式，找出治療IVF失敗與成功的規則，並藉以探討結果與因素之間的關係。本研究使用了決策樹演算法中的J48、隨機森林演算法工具分析IVF資料庫，預測準確率可達到82.5%，如果要給患者快速填選資料則準確率預測可達73.55%。預測出的結果除了可以提供給醫生和患者作為成功機率的預測參考，同時也可以從演算法分類結果看出影響成功率的特徵值，還可以對於未來醫療技術及品質的提升具有相當大的幫助。

Vitro Fertilization is the common name for in vitro fertilization and embryo transfer. The egg and sperm are removed and fertilized outside the body, and the embryo is divided and developed into an embryo in an incubator for 3 to 5 days, and then the embryo is implanted back into the mother's body, allowing the embryo to implant in the uterus. With today's advances and developments in the medical system, IVF treatment is still complex and unpredictable in terms of success rates. The purpose of this study is to apply Big Data techniques to analyze and construct outcome prediction models in IVF databases, to identify rules for failure and success in the treatment of IVF, and to examine the relationship between outcomes and factors. In this study, the J48, random forest algorithm tool of decision tree algorithm was used to analyze the IVF database, and the prediction accuracy could reach 82.5%, and the accuracy prediction could reach 73.36% if the patients were to fill in the data quickly. The prediction results can not only be used as a reference for doctors and patients to predict the success rate, but also can be used to improve the future medical technology and quality, in addition to identifying the characteristic values affecting the success rate from the classification results of the algorithm.

摘要................................i
Abstract...........................ii
誌謝..............................iii
目錄...............................iv
表目錄.............................vi
圖目錄............................vii
第一章 緒論.........................1
1.1研究背景與動機...................1
1.2研究目的.........................2
1.3研究流程.........................3
1.4研究範圍與限制...................5
第二章 文獻探討.....................6
2.1不孕症的定義.....................6
2.1.1不孕症影響的原因...............6
2.1.2試管嬰兒......................7
2.1.3不孕症相關分析研究.............7
2.2大數據(Big Data)................9
2.2.1大數據定義....................9
2.2.2大數據應用流程................11
2.3大數據分析能力定義..............11
2.3.1大數據分析能力相關研究.........12
2.4決策樹(Decision Tree, DT)......14
2.4.1決策樹架構....................14
2.4.2決策樹演算法類型..............15
第三章 研究方法....................20
3.1研究方法架構....................20
3.2資料來源........................22
3.3資料處理........................22
3.4系統環境資訊....................25
3.4.1 WEKA簡介....................25
3.4.2 WEKA文件格式.................27
3.5模型建立說明....................27
3.5.1 決策樹J48 (C4.5).............27
3.5.2 隨機森林(Random forest)......31
第四章 研究結果.....................34
4.1決策樹J48(C4.5)模型..............34
4.1.1 測試集模式....................34
4.1.2 交換驗證法....................38
4.1.3按比例分割驗證法...............40
4.2隨機森林模型.....................42
4.2.1 交換驗證法....................42
4.2.2 按比例分割驗證法...............45
4.3模型評估與分析....................47
4.3.1 J48(C4.5)演算法...............47
4.3.2隨機森林演算法..................49
第五章 結論與建議....................51
5.1研究結論.........................51
5.2研究建議.........................53
參考文獻............................54
Extended Abstract..................60

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