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| 題名: | 以心臟超音波為基礎使用類神經網路來預測心臟衰竭的住院機會 |
| 作者: | 楊弘宇 |
| 貢獻者: | 醫學資訊研究所 |
| 日期: | 2010 |
| 上傳時間: | 2010-09-09 11:25:16 (UTC+8) |
| 摘要: | 心臟衰竭是65歲以上的老人住院的主要原因之一，如果沒有好好控制，心臟衰及擁有相當高的死亡率，在診斷後的第一年，約有六分之一到五分之一會死亡，五年後平均有一半會死亡。但若治療後死亡率則降低很多，而且可以改善生活品質。在美國有將近500萬的人口有心臟衰竭，占了65歲以上人口約1%，而且每年以近50 萬人的數目增加，同時心臟衰竭也占了所有65歲以上人口住院原因的20%，在台灣40 歲以上的人口，每年有6%出現心臟衰竭 (Congestive Heart Failure) 症狀，總共約有50 萬病患。都普勒心臟超音波是一個最常用來評估心臟功能的方法，它可以讓我們了解到心臟的功能是否正常或是已經受損了。這個研究的主要目的是以心臟超音波的參數為基礎，使用類神經網路以及邏輯迴歸(Logistic Regression)的方式，來預測病人是否會因為心臟衰竭而住院，從2008年1月到2008年12月總共有7473個病人被納入這個試驗做為訓練組，另一組以2009年1月到12月當作是試驗組，總共有8124個病人，有15個心臟超音波的變數及兩個臨床變數被納入做為分析的參數，我們使用人工類神經網路(Artificial Neural Networks)的方式以及邏輯迴歸(Logistic Regression)的方式來加以分析判斷。最佳的類神經網路架構是前向式學習，倒傳類神經網路多層感知器是最好的架構，在預測病人是否會因心臟衰竭而住院方面，類神經網路的正確率是97.2%優於邏輯迴歸的96.6%，而以ROC 曲線下的面積大小來看，類神經網路是0.910±0.009優於邏輯迴歸的0.895±0.011，而p值 = 0.008是有意義的，所以我們認為用來預測病人是否會因心臟衰竭而住院方面，類神經網路顯然是一個比邏輯迴歸分析還要好的預測模式
Congestive heart failure (CHF) is one of the major causes of hospitalization in population older than 65 years. Doppler echocardiography is one of the objective method to evaluate cardiac function. These evaluations let us know whether heart function is normal or impaired. The aim of this study was to design an artificial neural network (ANN) model capable of predicting the exact possibility of hospitalization due to CHF. A total 7473 Patients were included in the study from Jan. 2008 to Dec. 2008 as training cases. Another 8124 patients collected from Jan. 2009 to Dec. 2009 as test cases. Fifteen echocardiographic variables and two clinical variables were collected from hospitalization patients. ANN model was set up by training the network with data from training set and subsequently testing with data from another test set to determine the optimal ANN architecture. The optimal ANN topology was found to be a standard feed-forward, fully-connected, back-propagation multilayer perceptron. The overall accuracy rate of ANN was 97.2%, which is higher than that of logistic regression (LR) (96.6%). By using the area under the receiver operating characteristics (ROC) curve as a measure of performance, the ANN outperformed the LR (0.910±0.009 versus 0.895±0.011; p = 0.008). Therefore, the CHF hospitalization prediction model using ANN performs significantly better than using LR. But this phenomenon mandates further evaluation and research on other CHF population from other hospitals. |
| 關聯: | 65頁 |
| 描述: | 標題 i
審定書 i
誌謝 ii
目錄 iii
表目錄 v
圖目錄 vi
論文摘要 viii
Abstract ix
第壹章 導論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
1.4 研究範圍與限制 7
1.5 論文架構 8
第貳章 文獻探討 9
2.1 類神經網路在臨床的應用 9
2.2 心臟衰竭的住院預測 11
2.2.1 在高心血管風險族群中以血糖值來預測病人的心臟衰竭住院機會 11
2.2.2 腎功能不全作為心臟衰竭再次住院的預測因子 12
2.2.3 升高肺動脈壓來預測心臟衰竭的住院機會及死亡率 12
2.3 使用類神經網路來預測病人因心臟衰竭而住院的問題 13
第叄章 研究方法 14
3.1 邏輯斯迴歸的理論基礎 15
3.1.1 迴歸-預測性的演算法 15
3.1.2 邏輯斯迴歸的基本模型 16
3.2.1生物神經網路 18
3.2.2 類神經網路的基本模型 19
3.2.3 類神經網路的特性 26
3.2.4 類神經網路的架構 27
第肆章 資料分析 29
4.1 心臟衰竭的住院條件 29
4.2 變數的分析與選擇 30
4.3 輸入變數的頻率分佈圖 39
4.4 邏輯斯迴歸模式分析 45
4.5 類神經網路模式分析 46
4.6 邏輯斯迴歸與類神經網路的分析比較 50
第伍章 總結 54
5.1 結論 54
5.2 未來研究方向 56
參考文獻 58
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