利用監督式機器學習預測身心疾病發生的可能性 -以Apex Legends為例

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高權宏(Chuan-Hung Kao) 查詢紙本館藏

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資訊管理學系

論文名稱

利用監督式機器學習預測身心疾病發生的可能性 -以Apex Legends為例
(Using Supervised Machine Learning to Predict the Possibility of Physical and Mental Disorder - Taking Apex Legends for Example)

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至系統瀏覽論文 (2026-10-1以後開放)

摘要(中)

本研究預測 Apex Legends 玩家的網路遊戲成癮、睡眠問題、廣泛性焦慮症發生的可能性。透過發放問卷的方式了解玩家們的身心疾病的狀況後，利用這些玩家填寫的量表分數，使用監督式機器學習的方式使用玩家們的遊戲數據做訓練，並預測發生上述身心症狀的可能性。本研究使用邏輯回歸、決策樹、隨機森林、XGBoost、Naïve Bayes去進行預測，發現隨機森林在預測網路遊戲成癮、睡眠問題、廣泛性焦慮症時有良好的表現，且在評估分類器的效能上，AUC的分數落在0.7上下，表示這些分類器能夠用在預測這些疾病上。此外本研究也會使用皮爾森相關係數去比對遊戲數據內哪些會與遊戲成癮、睡眠問題、廣泛性焦慮症有顯著的相關性的特徵。最後分析玩家族群、遊玩平台、年紀等等與本研究所探討的疾病之間的關聯。

摘要(英)

This research predicts the likelihood of Apex Legends players internet gaming disorder, sleep problems, and generalized anxiety disorder. By issuing questionnaires to understand the players’ physical and mental illnesses. Using their scale scores to train on their game statistic by supervised machine learning and predict the likelihood of having physical and mental disorder. It uses logistic regression, decision tree, random forest, XGBoost, and naïve bayes to make predictions in this research. It found that random forest has good performance in predicting internet gaming disorder , sleep problem and generalized anxiety disorder, and the AUC scores are 0.7, this situation means that this classifier can predict these diseases well. In addition, this research will also use the Pearson correlation coefficient to find which features in the game statistic are significantly correlated with internet gaming disorder, sleep problem, and generalized anxiety disorder. Finally, it analyzes the relationship between players, game platform, players’ age, etc. and find the relationship about these diseases in this research.

關鍵字(中)

★ 監督式機器學習
★ 網路遊戲成癮
★ 睡眠問題
★ 廣泛性焦慮症

關鍵字(英)

★ Supervised Machine Learning
★ Internet Gaming Disorder
★ Sleep Problem
★ Generalized Anxiety Disorder

論文目次

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
1. 緒論 1
1-1　研究背景 1
1-2　研究動機 2
1-3　研究目的 3
2. 文獻探討 5
2-1　網路遊戲成癮與遊戲類型 5
2-2　睡眠障礙 8
2-3　廣泛性焦慮症 9
2-4　機器學習介紹 9
2-5　不平衡資料集的處理方式 11
3. 研究方法 15
3-1　研究流程 15
3-2　研究對象和資料收集 16
3-3　問卷內容說明 16
3-4　變數說明 22
3-5　資料集處理 26
3-6　演算法分類模型 28
3-7　評估指標 30
4. 研究結果 33
4-1　模型預測結果 33
4-2　相關係數結果 34
5. 結論 38
5-1　研究結論 38
5-2　研究限制 39
參考文獻 41
附錄 47

參考文獻

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指導教授

周惠文(Huey-Wen Chou)

審核日期

2021-10-5

推文