Hybrid Regression Models for Predicting Hydration: A Case Study in Pediatric Hemodialysis

Figures & Tables

	Real values	EN values	GBR values	SVR values	EN-GBR values	EN-SVR values
Patient A	−0.3	−0.48129796	−0.18628015	−0.81704017	−0.3527895	−0.46503815
	−0.8	−0.91890762	−0.80598051	−1.27369152	−0.80427712	−0.90426637
	0.2	0.50873695	0.23782386	0.39220703	0.20545681	0.46932447
	−1.2	−1.11180823	−1.16581552	−1.27910948	−1.18921171	−1.13263351
	−2.1	−2.2058463	−2.09972043	−1.83435081	−2.09690962	−2.19735346
	−2.2	−2.30081989	−2.18690249	−1.91096666	−2.1958786,	−2.30016087
	−5.6	−5.05723497	−5.59310215	−3.86500347	−5.59597978	−5.12703839
	−2.8	−2.83149824	−2.79700751	−2.38790569	−2.79852713	−2.84700099
	−3.1	−3.28661257	−3.10436425	−2.90010456	−3.08728966	−3.21576016
	1.1	1.13724622	1.08674662	0.91430495	1.08914686	1.13514346

Patient B	2.3	2.05498245	2.30474957	2.07372147	2.30325372	2.32446905
	0.6	0.67973341	0.62278249	0.72046415	0.61362807	0.70735133
	−1.1	−0.98525658	−1.16755784	−1.07055426	−1.10503825	−1.14305158
	−1.3	−1.19201570	−1.40885596	−1.32889134	−1.32276658	−1.37031130
	−1.8	−1.72955661	−1.73768376	−1.84493265	−1.80881611	−1.86989233
	−1.7	−1.66446912	−1.69278792	−1.80055228	−1.73364828	−1.81502659
	0.9	0.85001765	0.93925670	0.88927059	0.91509444	0.92993130
	−3.8	−3.84511421	−3.89311761	−3.89983185	−3.79272144	−3.84172986
	−4.7	−4.97572847	−4.68874558	−4.75482693	−4.72968422	−4.79968123
	−2.2	−2.16571580	−2.17217104	−2.26907722	−2.20450492	−2.20647985

R²	RMSE	MAPE
1−∑i=1mYι^−Yi2∑i=1mY¯−Yi2 1 - {{\sum\nolimits_{i = 1}^m {{{\left( {\widehat {{Y_\iota }} - {Y_i}} \right)}^2}} } \over {\sum\nolimits_{i = 1}^m {{{\left( {\bar Y - {Y_i}} \right)}^2}} }}	1m∑i=1mYi−Yι^2 \sqrt {{1 \over m}\sum\limits_{i = 1}^m {{{\left( {{Y_i} - \widehat {{Y_\iota }}} \right)}^2}} }	1m∑i=1mYi−Yι^Yi⋅100 {1 \over m}\sum\limits_{i = 1}^m {\left\| {{{{Y_i} - \widehat {{Y_\iota }}} \over {{Y_i}}}} \right\| \cdot 100}

	Model	R²	RMSE	MAPE
Patient A	EN	0.97770	0.33319	0.103
	GBR	0.99969	0.50409	0.012
	SVR	0.92285	0.53131	0.095
	EN-GBR	0.99960	0.16218	0.007
	EN-SVR	0.98259	0.30489	0.092
	Ridge regression	0.91005	0.44157	0.243
	Kernel Ridge	0.90765	0.43811	0.257
	Bayesian Ridge	0.89345	0.45118	0.256
	RF	0.68556	0.46803	0.433
	LSTM	0.95325	0.38197	0.136

Patient B	EN	0.97998	0.28097	0.055
	GBR	0.99802	0.27370	0.015
	SVR	0.97227	0.54102	0.141
	EN-GBR	0.99919	0.10223	0.011
	EN-SVR	0.96178	0.37549	0.053
	Ridge regression	0.97089	0.34216	0.062
	Kernel Ridge	0.96991	0.34059	0.063
	Bayesian Ridge	0.89360	0.46269	0.055
	RF	0.93517	0.35403	0.789
	LSTM	0.84637	0.32715	0.408