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研究生:柯奕吉
研究生(外文):Ko, Yi-Ji
論文名稱:應用機器學習於慢性阻塞性肺病預測模型之建立
論文名稱(外文):Machine Learning for Chronic Obstructive Pulmonary Disease Prediction
指導教授:杜清敏杜清敏引用關係
指導教授(外文):Duh, Ching-Miin
口試委員:張吉成鄭夙芬杜清敏
口試委員(外文):Chang, Chi-ChengCheng, Su-FenDuh, Ching-Miin
口試日期:2020-07-07
學位類別:碩士
校院名稱:國立臺北護理健康大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:81
中文關鍵詞:慢性阻塞性肺病機器學習輔助診斷危險因子監控系統資料視覺化
外文關鍵詞:COPDMachine LearningDiagnosis Aid QuestionnaireBRFSSData Visualization
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根據世界衛生組織指出,2016年全球的慢性阻塞性肺病(Chronic Obstructive Pulmonary Disease,COPD)病例達到2.51億例,為目前全球第三大死因。美國國家心臟、肺和血液研究所指出起初COPD可能不會有任何症狀,且與其他疾病容易混淆,使得患者常常未能在第一時間得到適切的診斷,延誤治療的時機。
現今COPD臨床診斷流程是依患者症狀、病因再加上肺功能檢查來綜合評估診斷,但因患者之間的個別健康與病因狀況差異很大,且目前對於COPD的病因仍未清楚了解,於是有了評估性問卷的方式來輔助診斷。目前有許多研究指出使用評估性問卷,更確實可以在一般情況下發現疑似COPD患者,提高當前診斷準確性以期能達精準醫療(precision medicine)之成效,可惜的是目前模型準確度與後續實際即時臨床診斷系統服務之應用似乎略顯不足。
因此,本研究發展出利用美國疾病管制與預防中心(Center of Disease Control and Prevention,CDC)危險因子監控系統(Behavioral Risk Factor Surveillance System,BRFSS)的資料建立一套機器學習預測模型,除了找出影響COPD預測的關鍵因素外,並應用SHAP(Shapley additive explanations)技術來分析解釋特徵是如何影響最終模型預測結果,最後以最佳模型為基礎,建置COPD輔助診斷系統平台。本系統提供視覺化輔助診斷結果報告,能直接查看每項特徵風險的權重大小,使用者能以視覺方式理解每項特徵因素對預測的影響,並藉以輔助醫師診斷或提供民眾能自行判斷是否需要就醫,因而能早日取得治療契機,提高國家醫療資源效能,減輕家庭與長期照護之負擔。
According to the World Health Organization (WHO), 251 million cases of Chronic Obstructive Pulmonary Disease (COPD) occurred worldwide in 2016, making it the world's third leading cause of death. According to the National Heart, Lung, and Blood Institute, the COPD may not present any significant symptoms, thus many patients may not be confirmed at the beginning lead to delaying treatment.
Nowadays the confirmation of COPD needed following clinical symptoms, etiology and lung function. However, due to the differences in individual health among patients, and the causes of COPD are still not clearly understood at present, the evaluative questionnaire is adopted to assist the diagnosis. At present, many studies have pointed out that the use of an evaluative questionnaire can find suspected COPD patients in general conditions effectively, it improves the current diagnostic accuracy and achieves the effect of precision medicine. Unfortunately, the application of a combination of the accuracy model and the clinical diagnosis system services seems insufficient.
Therefore, this study employed several Machine Learning predictions to find a best fit COPD prediction model based on the data of the Behavioral Risk Factor Surveillance System (BRFSS) of the Center for Disease Control and Prevention (CDC). In addition to identifying the key factors affecting the prediction of COPD, Application SHAP (Shapley additive explanations) technology is used to explain the impact of features in final model-predicted results. Finally, based on the best model, applied to build a COPD web-based diagnosis-aid system platform, the system provides visual supporting diagnosis message, it can measure the weight of each risk characteristics, the user can visually realize each characteristic factor's influence on the prediction, thus may assist in physician diagnosis and provide people self-evaluation the necessity to go to the doctor for early treatment. Therefore, promote the efficiency of national health resources and alleviate the family’s financial burden in long-term care.
誌 謝 I
摘 要 II
Abstract IV
目 錄 VI
圖目錄 VIII
表目錄 X
第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究目的 4
第四節 研究流程 4
第五節 重要名詞解釋 6
第貳章 文獻探討 8
第一節 COPD定義與介紹 8
第二節 行為危險因子監控系統 (BRFSS) 17
第三節 人工智慧與機器學習 17
第四節 模型評估與選擇 24
第參章 研究方法 30
第一節 研究工具 31
第二節 資料來源 31
第三節 變項挑選 32
第四節 資料預處理 33
第五節 機器學習模型訓練與評估 38
第六節 輔助診斷系統平台建置 43
第肆章 研究結果 45
第一節 統計分析 45
第二節 特徵選擇 48
第三節 模型結果 49
第四節 輔助診斷系統平台 59
第伍章 討論 63
第一節 模型比較與特徵探討 63
第二節 本研究之應用性 69
第三節 研究限制 69
第陸章 結論 70
第一節 研究結論 70
第二節 研究貢獻 71
第三節 未來研究方向與研究建議 71
參考文獻 72
附錄 76
附錄一 2018 BRFSS變項問題 76
附錄二 COPD輔助診斷系統平台 80


中文部分
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