 |
English
|
正體中文
|
简体中文
|
全文筆數/總筆數 : 46833/50693 (92%)
造訪人次 : 11844052
線上人數 : 586
|
|
|
資料載入中.....
|
請使用永久網址來引用或連結此文件:
https://irlib.pccu.edu.tw/handle/987654321/26874
|
| 題名: | The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly |
| 作者: | Hsieh, KC (Hsieh, Kuen-Chang)
Chen, YJ (Chen, Yu-Jen)
Lu, HK (Lu, Hsueh-Kuan)
Lee, LC (Lee, Ling-Chun)
Huang, YC (Huang, Yong-Cheng)
Chen, YY (Chen, Yu-Yawn) |
| 貢獻者: | Grad Inst Sport Coaching Sci |
| 關鍵詞: | Back Propagation Artificial Neural Network (BP-ANN)
Dual-energy X-ray absorptiometry
Bioelectrical impedance analysis (BIA)
Body composition
Elderly |
| 日期: | 2013-02-06 |
| 上傳時間: | 2014-02-27 13:36:23 (UTC+8) |
| 摘要: | Background: This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method.
Methods: A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models.
Results: The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r(2) = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r(2) = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA.
Conclusion: When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r(2) with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. |
| 關聯: | NUTRITION JOURNAL Volume: 12 Article Number: 21 |
| 顯示於類別: | [運動教練研究所] 期刊論文
|
文件中的檔案:
| 檔案 |
描述 |
大小 | 格式 | 瀏覽次數 |
|---|
| index.html | | 0Kb | HTML | 420 | 檢視/開啟 |
|
在CCUR中所有的資料項目都受到原著作權保護.
|
