Abstract
Artificial neural network (ANN) is increasingly applied in clinical medicine. We therefore constructed an ANN to predict intracellular water (ICW) volume in 44 healthy Taiwaners. Demographic and anthropometric data were recorded as predictors, and ICW volume measured by bioelectrical impedance analysis (ICW-BIA) was the reference. ICW volume predicted by ANN (ICW-ANN) was compared with ICW-BIA. ICW-BIA (21.26 ± 0.58l) and ICW-ANN (21.25 ± 0.57l) was insignificantly different (p = 0.76). ICW-BIA and ICW-ANN were strongly correlated (r = 0.94, p <0.0001) with a significant agreement (mean difference, 0.01; lower and upper limits of agreement, -2.31 and 2.33) in Bland-Altman plot. Passing-Bablok regression was described as ICW-BIA = 1.04 × ICW-ANN-0.49, with 95% confidence interval for slope 0.94-1.14 and for intercept -2.76-1.49, indicating that both methods were interchangeable. ANN provided an excellent alternative of BIA to predict ICW volume in healthy subjects.
| Original language | English |
|---|---|
| Pages (from-to) | 1231-1238 |
| Number of pages | 8 |
| Journal | International Journal of Clinical Practice |
| Volume | 60 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Oct 2006 |
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Keywords
- Anthropometry
- Bioelectrical impedance
- Intracellular water
- Neural network
ASJC Scopus subject areas
- Medicine(all)
Cite this
Neural network technology to predict intracellular water volume. / Chiu, J. S.; Chen, C. A.; Lee, C. H.; Li, Y. C.; Lin, Y. F.; Wang, Y. F.; Yu, Fu Chiu.
In: International Journal of Clinical Practice, Vol. 60, No. 10, 10.2006, p. 1231-1238.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Neural network technology to predict intracellular water volume
AU - Chiu, J. S.
AU - Chen, C. A.
AU - Lee, C. H.
AU - Li, Y. C.
AU - Lin, Y. F.
AU - Wang, Y. F.
AU - Yu, Fu Chiu
PY - 2006/10
Y1 - 2006/10
N2 - Artificial neural network (ANN) is increasingly applied in clinical medicine. We therefore constructed an ANN to predict intracellular water (ICW) volume in 44 healthy Taiwaners. Demographic and anthropometric data were recorded as predictors, and ICW volume measured by bioelectrical impedance analysis (ICW-BIA) was the reference. ICW volume predicted by ANN (ICW-ANN) was compared with ICW-BIA. ICW-BIA (21.26 ± 0.58l) and ICW-ANN (21.25 ± 0.57l) was insignificantly different (p = 0.76). ICW-BIA and ICW-ANN were strongly correlated (r = 0.94, p <0.0001) with a significant agreement (mean difference, 0.01; lower and upper limits of agreement, -2.31 and 2.33) in Bland-Altman plot. Passing-Bablok regression was described as ICW-BIA = 1.04 × ICW-ANN-0.49, with 95% confidence interval for slope 0.94-1.14 and for intercept -2.76-1.49, indicating that both methods were interchangeable. ANN provided an excellent alternative of BIA to predict ICW volume in healthy subjects.
AB - Artificial neural network (ANN) is increasingly applied in clinical medicine. We therefore constructed an ANN to predict intracellular water (ICW) volume in 44 healthy Taiwaners. Demographic and anthropometric data were recorded as predictors, and ICW volume measured by bioelectrical impedance analysis (ICW-BIA) was the reference. ICW volume predicted by ANN (ICW-ANN) was compared with ICW-BIA. ICW-BIA (21.26 ± 0.58l) and ICW-ANN (21.25 ± 0.57l) was insignificantly different (p = 0.76). ICW-BIA and ICW-ANN were strongly correlated (r = 0.94, p <0.0001) with a significant agreement (mean difference, 0.01; lower and upper limits of agreement, -2.31 and 2.33) in Bland-Altman plot. Passing-Bablok regression was described as ICW-BIA = 1.04 × ICW-ANN-0.49, with 95% confidence interval for slope 0.94-1.14 and for intercept -2.76-1.49, indicating that both methods were interchangeable. ANN provided an excellent alternative of BIA to predict ICW volume in healthy subjects.
KW - Anthropometry
KW - Bioelectrical impedance
KW - Intracellular water
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=33748547543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33748547543&partnerID=8YFLogxK
U2 - 10.1111/j.1742-1241.2005.00761.x
DO - 10.1111/j.1742-1241.2005.00761.x
M3 - Article
C2 - 16981968
AN - SCOPUS:33748547543
VL - 60
SP - 1231
EP - 1238
JO - International Journal of Clinical Practice
JF - International Journal of Clinical Practice
SN - 1368-5031
IS - 10
ER -