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系統識別號 U0026-2806201819233200
論文名稱(中文) 利用自適性類神經模糊推論系統建構睡眠呼吸中止症臨床診斷預測模型
論文名稱(英文) Establishing a Clinical Prediction Model of Sleep Apnea Syndrome by Adaptive Neuro-Fuzzy Inference System
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 106
學期 2
出版年 107
研究生(中文) 王瑭毅
研究生(英文) Tang-Yi Wang
學號 N96044125
學位類別 碩士
語文別 英文
論文頁數 82頁
口試委員 指導教授-莊哲男
口試委員-陳昭羽
口試委員-劉文德
中文關鍵字 睡眠呼吸中止症  自適性模糊邏輯推論系統  人體體型  性別  年齡  睡眠呼吸中止指數  特徵選擇 
英文關鍵字 Sleep apnea  Adaptive Neural Fuzzy Inference System (ANFIS)  anthropometric measurement feature  gender  age  Apnea Hypopnea Index (AHI)  feature selection 
學科別分類
中文摘要 由於近年來,睡眠問題逐漸受到大眾注視,睡眠呼吸中止症也已成為不可忽視的健康問題之一。雖然透過睡眠檢查可以得到較精準的診斷結果,但其過程相當繁瑣且昂貴,此外,利用臨床資料建立準確的預測模型仍然是一個難題。故本研究的目的是利用臨床上容易取得的參數,透過自適性類神經模糊推理系統(ANFIS)來預測睡眠呼吸中止症的嚴重程度,預期研究的結果將有助將有助於醫生的臨床判斷,進而對患者安排後續的診斷與治療計畫。

此次研究主要包括三個部分:第一部分是資料庫的前處理,輸入預測模型的特徵參數,包括人體身型數據,年齡,問卷結果和患者臨床資料,並計算這些參數與睡眠呼吸中止指數之間的單變量相關性。第二部分是構建多個合併不同輸入特徵參數組合的ANFIS模型來得到特徵的預測效果分佈圖,最後一部分是模型驗證。

結果顯示,按性別和年齡劃分來探討特徵參數與結果之間的相關性,可得到更為精確的預測結果,其中青壯年女性的最佳敏感度和特異度分別為65.6%和90%(老年女性分別為74.1%和61.3%)。青年男性的結果分別為79.1%和77.1%以及老年男性為82%和60%。另外,對於預測睡眠呼吸中止症時,頸圍乘以腰圍除以身高、早晨的舒張壓和血氧飽和度可以考慮為有影響性的特徵。
英文摘要 In recent years, getting a good sleep becomes one of the important issues and there are more researches to study sleep disorders. Obstructive sleep apnea (OSA) is one of the sleep disorders that has attracted much attention. However, the diagnosis of OSA is still limited in the daily clinical practice. Although we can obtain more accurate diagnosis through the examination in the hospital’s sleep lab, it is very time-consuming and expensive to undertake one. Besides, developing an accurate prediction model for OSA is still a difficult task in clinical trial.

The purpose of this thesis is to use the machine learning method based on the Adaptive Neuro Fuzzy Network Inference System (ANFIS) to establish a predicting model for the severity of OSA and to explore the correlation between input variables and results divided by genders and age. Therefore, the result will be helpful for medical doctors to make medical decisions, and then the OSA patients could receive the corresponding and suitable follow-up treatment more efficiently.

There are three main parts in the research. The first part is the pre-processing of database including anthropometric measurement features, age, questionnaire scores and clinical data of patients, and calculating the single variable correlation between features and the Apnea Hypopnea Index (AHI). The second part is to build up several ANFIS models with different combinations of all input features to describe the distribution of features, and the last part is the validation of model.

The results show that the correlation between the characteristic parameters and the results can be found by gender and age, and more accurate prediction results can be obtained. The best sensitivity and specificity in young female is 65.6% and 90% (respectively 74.1% and 61.3% in elder female) for the AHI threshold 15. For male, the result is 79.1% and 77.1% in young male (respectively 82% and 60% in elder male) for the AHI threshold 30. In addition, neck circumference multiplied by waist circumference divided by height (NWH), diastolic blood pressure in the morning and the degree of blood oxygen saturation can be considered to be the influential features for predicting sleep apnea.
論文目次 Abstract in Chinese .............i
Abstract in English .............ii
Acknowledgements .............iv
Contents ...............vi
List of Tables ..............viii
List of Figures ..............x
1 Introduction .............1
1.1 Background Knowledge and Motivation .......1
1.2 Research Objectives and Approach ........4
1.3 Thesis Contribution ..........5
1.4 Thesis Organization ..........6
2 Materials .............7
2.1 Clinical Data ...........7
2.2 Data Collection ............8
2.3 Data Preprocessing ..........9
3 Methodology ............15
3.1 Data Analysis via Single Variable Correlation ......15
3.1.1 Pearson Product-Moment Correlation ......16
3.1.2 Spearman’s Rank Correlation Coefficient ......17
3.2 Modeling .............18
3.2.1 Linear Equation and the Coefficient of Determination ....18
3.2.2 Adaptive Neural Fuzzy Inference System (ANFIS) ...20
3.3 Validation .............29
3.3.1 k-fold Cross-Validation ........30
3.3.2 Confusion Matrix .........31
4 Result ..............38
4.1 Feature Analysis ...........38
4.2 Result of Modeling ..........44
4.3 Result of Prediction ..........60
5 Discussions and Conclusions ..........66
References ..............76
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