臺灣博碩士論文加值系統

背景：
近年來年輕族群患有憂鬱症的比例已不容忽視，且於青少年罹患憂鬱症有相當高的比例會在其成人復發，因此早期發現憂鬱傾向已成一重要議題。使用機器學習預測憂鬱傾向的研究於近幾年如雨後春筍般冒出，但研究對象的年齡層常針對中、老年人，較少針對年輕族群，且多數為橫斷面研究，缺乏利用縱貫性資料觀察時序性變化。故本研究採用中央研究院社會學研究所的縱貫性資料庫臺灣青少年成長歷程研究（Taiwan Youth Project [TYP]）進行憂鬱傾向的探討。
方法：
本研究從臺灣青少年成長歷程研究計畫中篩選出1,047名研究對象，觀察青少年的成長歷程如何影響成人期的憂鬱傾向。以SAS 9.4進行自變項與依變項的雙變項分析，且鑒於社交網路和憂鬱傾向常互相影響，本研究將利用深度學習中圖神經網路（graph neural network [GNN]）的方法，以社交網路結合各重要因子進行圖卷積網路、圖取樣與聚合、圖同構網路、圖注意力網路的模型建置，來預測其成年時期的憂鬱傾向，並同時建置機器學習中的邏輯斯迴歸、支援向量機、隨機森林、極限梯度提升進行比較。模型皆使用Python 3.8進行十折交叉驗證做驗證方式，採用上採樣、下採樣、上下採樣、調整權重的方式進行資料平衡。
結果：
於1,047名研究對象中，有950人（90.74%）不具憂鬱傾向，97人（9.26%）具憂鬱傾向。雙變項分析結果整理出影響成人期憂鬱傾向的因素，居住地、偏差行為、自尊、自我滿意程度、快樂感、健康狀態、生活事件、生命重大事件的發生與影響、青少年時期憂鬱傾向、家庭同住狀況、家庭經濟條件、家庭凝聚力、家庭支持、家庭間滿意程度、朋友支持、同班好友數量與成人期憂鬱傾向有顯著相關。預測模型方面，於測試集中表現最好的為圖注意力網路（AUC值：0.7356），LR-reweighting和SVM-reweighting的表現次佳（AUC值：0.7158）。在模型重要特徵排名中，自尊、自我滿意程度、家庭凝聚力、家庭支持、快樂感、自我滿意程度、生命重大事件的影響程度、性別、健康狀態、生活事件皆是自變項中影響模型預測的重要特徵。
結論：
本研究驗證了圖神經網路在縱貫性資料也能獲得良好的表現，未來研究可利用於時間序列上有較佳表現的長短期記憶（long short-term memory [LSTM]）進行比較。從重要特徵排名中亦可發現，除性別變項外，其餘變項在雙變項分析中皆達到統計顯著。針對影響成人期憂鬱傾向的高相關因子，除青少年照護者、家庭、老師、學校、社區等最直接與青少年互動的接觸者和場所，政府機關、社福機構，也可對相關領域進行關切，制定相應的法規或提供相關福利，在規範之餘提供心理輔導，盡早進行治療，以此降低國內年輕族群罹患憂鬱症的風險，避免病情反覆發作，甚至最後引起自殺行為。

Background:
The proportion of young generation suffering in depression becomes higher in recent years, and would be relapsed in their adulthood. Early detection of depression has been becoming an important issue. Researchers emploied machine learning techniques to predict depression; howerver, the study subjects are often in middle-aged or elderly population instead of young generation. Moreover, most of the researches are cross-sectional studies, which may lack to observe temporal changes as the longitudinal studies. Therefore, this study was derived the data from Taiwan Youth Project (TYP), a longitudinal database from the Institute of Sociology, Academia Sinica, to investigate the tendency of depression. This study aims to observe how the adolescent’s developmental trajectory affects the depression in adulthood, and to build up a predictive model via the machine learning techniques.
Method:
A total of 1,047 subjects were retrieved from the TYP database. Bivariate analysis was was applied to examinate the associations of independent variables and dependent variable (depression in adulthood) by SAS 9.4. According to the interactions of social networks and depression, this study applied the graph neural network (GNN), which is combined various important factors in social networks and to constructe graph convolutional network (GCN), graph sample and aggregate (GraphSAGE), graph isomorphism network (GIN), and graph attention network (GAT) models to predict subjects’ depression in their adulthood. Meanwhile, this study also constructed the following four machine learning predictive models: logistic regression, support vector machine, random forest, extreme gradient boosting. All predictive models were used 10-fold cross-validation and data balance techniques, including oversampling, undersampling, oversampling and undersampling, and reweighting. The analysis is conducted using Python 3.8.
Result:
The number of non-depression and depression individuals were 950 (90.74%) and 97 (9.26%) in 1,047 subjects, respectively. Bivariate analysis results showed that residence, deviant behavior, self-esteem, self-satisfaction, happiness, health status, life events, occurrence and influence of life events, depression in adolescence, family living conditions, family economic conditions, family cohesion, family support, family satisfaction, friend support, and the number of classmates have associations with adulthood depression. The best performance predictive model in the test dataset is the GAT model (AUC: 0.7356). The ranking of important features of the GAT model include: self-esteem, self-satisfaction, family cohesion, family support, happiness, self-satisfaction, influence of life events, gender, health status, and life events.
Conclusion:
This study demonstrated that GNN has a good performance in longitudinal database. Future researchers may conduct the long short-term memory (LSTM) which has better performance in time series is a potential research area. Comparing to the results of bivariate analysis and important features in GAT model, only gender is not significance in bivariate analysis. The caregivers, families, teachers, schools, communities those who are directly interact with adolescents, government agencies and social welfare organizations should also pay more attentions, formulated corresponding regulations, and provided related psychological counseling to adolescents. Early interventions could reduce the risk of depression among young generation and, avoid recurrence of the disease and even leading to suicidal behavior.

誌謝 i
摘要 ii
Abstract iv
目錄 vi
圖目錄 ix
表目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的與問題 7
第二章 文獻探討 8
2.1 憂鬱症 8
2.1.1 憂鬱症現況與概述 8
2.1.2 憂鬱症狀量表 11
2.1.3 憂鬱症狀相關因子 26
2.1.4 小結 35
2.2 社交網路 47
2.2.1 社交網路概述 47
2.2.2 社交網路應用於心理狀態的文獻 48
2.2.3 社交網路對心理狀態的影響 51
2.2.4 小結 52
2.3 機器學習 55
2.3.1 機器學習概述 55
2.3.2 憂鬱預測相關文獻 56
2.3.3 圖神經網路概述 60
2.3.4 圖神經網路的困難和挑戰 64
2.3.5 圖神經網路相關文獻 65
2.4 憂鬱狀態、社交網路、機器學習 71
2.4.1. 憂鬱狀態與社交網路 71
2.4.2. 憂鬱狀態與機器學習 72
2.4.3. 社交網路與機器學習 72
第三章　研究方法 74
3.1 研究流程與架構 74
3.1.1 研究流程 74
3.1.2 研究架構 77
3.2 研究對象 79
3.2.1 資料來源 79
3.2.2 資料前處理 83
3.3 變項操作型定義 85
3.3.1 變項定義 85
3.3.2 建構社交網路 91
3.4 統計方法 92
3.5 預測模型技術 92
3.5.1 邏輯斯迴歸（logistic regression [LR]） 93
3.5.2 支援向量機（support vector machine [SVM]） 93
3.5.3 隨機森林（random forest [RF]） 94
3.5.4 極限梯度提升（extreme gradient boosting [XGBoost]） 95
3.5.5 圖卷積網路（graph convolutional network [GCN]） 96
3.5.6 圖取樣與聚合（graph sample and aggregate [GraphSAGE]） 96
3.5.7 圖同構網路（graph isomorphism network [GIN]） 98
3.5.8 圖注意力網路（graph attention network [GAT]） 99
3.6 資料平衡技術 100
3.7 評估指標 101
第四章 研究結果 103
4.1 描述性統計 103
4.1.1 個人層級變項分布情形 103
4.1.2 家庭層級變項分布情形 107
4.1.3 同儕層級變項分布情形 109
4.2 雙變項分析 110
4.2.1 個人層級變項與成人期是否具憂鬱傾向 111
4.2.2 家庭層級變項與成人期是否具憂鬱傾向 114
4.2.3 同儕層級變項與成人期是否具憂鬱傾向 118
4.3 社交網路圖 120
4.4 機器學習模型實驗結果 122
4.5 預測模型重要特徵排名 129
第五章 討論與建議 131
5.1 討論 131
5.2 研究限制 135
5.3 建議 135
5.4 結論 136
參考文獻 138
中文文獻 138
英文文獻 140

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