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研究生:郭庭佑
研究生(外文):Ting-You Guo
論文名稱:發展理論模式以預測妊娠糖尿病發
論文名稱(外文):Development of Theoretical Models to Predict the Onset of Gestational Diabetes Mellitus
指導教授:梁剛荐
指導教授(外文):Max K. Leong
學位類別:碩士
校院名稱:國立東華大學
系所名稱:生命科學系
學門:生命科學學門
學類:生物學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
論文頁數:54
中文關鍵詞:妊娠糖尿病機器學習
外文關鍵詞:Gestational diabetes mellitusMachine Learning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:190
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婦女懷孕生產時容易病發妊娠性糖尿病(Gestational Diabetes Mellitus, GDM),在根據Beischer et al. (1991)研究報告,婦女在的懷孕過程中,有1–3%的機率會罹患妊娠性糖尿病,娠糖尿病好發於婦女懷孕24–28週期間[1]。妊娠糖尿病的婦女及其後代容易造成身體上的危害,因此開發一個模型準確和快速來預測懷孕婦女產前是否為妊娠期糖尿病高危險群,即早做出預防。本研究目的乃利用機器學習方法建構一個模型,結果發現由decision tree的模型可成功預測妊娠期糖尿病,在training set (n = 3332, sensitivity = 0.969, Specificity = 0.996)及test set (n = 3305, sensitivity = 0.949, Specificity = 0.995)。所以本篇發展的decision tree模型可運用在預測妊娠期糖尿病上。
Gestational diabetes mellitus (GDM) is one of the most frequent medical complications in pregnancy the prevalence about 1–3% of pregnant women and further harm to the fetus Beischer et al. (1991). The necessity of developing a model to predict GDM can prevent the patient suffering and healthcare expenditure. The objective of this study was to develop one good fit model for the prognosis of GDM. The predictions by the decision tree model are in good agreement with the observed values in the training set (n = 3332; sensitivity = 0.969; Specificity = 0.996), test set (n = 3305; sensitivity = 0.949; Specificity = 0.995). The results indicate that the decision tree model performed better precision than that of other models, suggesting that the decision tree can be employed as a theoretical model tool to predict GDM.
摘要 i
關鍵字 i
Keywords iii
Abbreviations v
1.Introduction 1
2 Research design and methods 7
2.1 Set Selection 7
2.2 Decision Tree 7
2.3 Logistic regression 9
2.4 k-nearest neighbors (kNN) 9
2.5 Naive Bayes classifier 9
2.6 Support Vector Machine (SVM) 10
2.7 Qualitative Model Validation 10
3.Results 13
3.1 Patients and data 13
3.2 Decision Tree 13
3.3 Logistic regression 14
3.4 k-nearest neighbors algorithm 14
3.5 Naive Bayes classifier 14
3.6 Support Vector Machine 16
4.Discussion 17
5.Conclusions 19
6.References 21
摘要 i
關鍵字 i
Keywords iii
Abbreviations v
1.Introduction 1
2 Research design and methods 7
2.1 Set Selection 7
2.2 Decision Tree 7
2.3 Logistic regression 9
2.4 k-nearest neighbors (kNN) 9
2.5 Naive Bayes classifier 9
2.6 Support Vector Machine (SVM) 10
2.7 Qualitative Model Validation 10
3.Results 13
3.1 Patients and data 13
3.2 Decision Tree 13
3.3 Logistic regression 14
3.4 k-nearest neighbors algorithm 14
3.5 Naive Bayes classifier 14
3.6 Support Vector Machine 16
4.Discussion 17
5.Conclusions 19
6.References 21


Figure 1. Histograms of (a) age, (b) education, (c) body mass index (BMI), (d) Gestation, (e) parity, (f) DM, (g) OGTT, and (h) Pregnancy-induced hypertension (PIH) in total form for all pregnant women in the training set and test set. 33
Figure 2. The prediction of GDM by decision tree. 34

摘要 i
關鍵字 i
Keywords iii
Abbreviations v
1.Introduction 1
2 Research design and methods 7
2.1 Set Selection 7
2.2 Decision Tree 7
2.3 Logistic regression 9
2.4 k-nearest neighbors (kNN) 9
2.5 Naive Bayes classifier 9
2.6 Support Vector Machine (SVM) 10
2.7 Qualitative Model Validation 10
3.Results 13
3.1 Patients and data 13
3.2 Decision Tree 13
3.3 Logistic regression 14
3.4 k-nearest neighbors algorithm 14
3.5 Naive Bayes classifier 14
3.6 Support Vector Machine 16
4.Discussion 17
5.Conclusions 19
6.References 21


Figure 1. Histograms of (a) age, (b) education, (c) body mass index (BMI), (d) Gestation, (e) parity, (f) DM, (g) OGTT, and (h) Pregnancy-induced hypertension (PIH) in total form for all pregnant women in the training set and test set. 33
Figure 2. The prediction of GDM by decision tree. 34


Table 1. The incidence of GDM (2008-2012). 35
Table 2. Yearly cumulative incidence of gestational diabetes mellitus by age 14–50 Years. 39
Table 3. Yearly Cumulative Incidence of Gestational Diabetes Mellitus by BMI <18.5-≧35.0 Years. 43
Table 4. Descriptors selected as the input of five models and their factors. 48
Table 5. Statistic parameters by Decision tree in Training set and Test set. 49
Table 6. Statistic parameters by logistic regression in Training set and Test set. 50
Table 7. Statistic parameters by kNN in Training set and Test set. 51
Table 8. Statistic parameters by Naive bayes in Training set and Test set. 52
Table 9. Statistic parameters by SVM in Training set and Test set. 53
Table 10. 10-fold cross-validation evaluated by 5 models. 54





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