Table 2.

REFINED-CNN performance comparison with competing models for the GDSC dataset.

Model	NRMSE (20%)	NRMSE (50%)	NRMSE (80%)	PCC (20%)	PCC (50%)	PCC (80%)	Bias (20%)	Bias (50%)	Bias (80%)
EN	0.890	0.889	0.887	0.488	0.484	0.486	0.848	0.849	0.840
RF	0.609	0.620	0.569	0.797	0.785	0.821	0.433	0.417	0.337
SVR	0.750	0.742	0.525	0.847	0.845	0.853	0.257	0.273	0.241
ANN	1.407	0.475	0.435	0.519	0.883	0.901	0.784	0.153	0.233
Random-CNN	0.579	0.456	0.441	0.836	0.892	0.903	0.215	0.193	0.222
PCA-CNN	0.612	0.461	0.443	0.820	0.891	0.901	0.201	0.228	0.179
REFINED-CNN	0.541	0.439	0.414	0.845	0.899	0.911	0.255	0.173	0.197

The numbers in parentheses indicate the percentages of the available data used for training.

EN elastic net, RF random forest, SVR support vector regression, ANN artificial neural networks, Random-CNN random mapping based convolutional neural network, PCA-CNN principal component analysis based convolutional neural networks, REFINED-CNN proposed REFINED approach-based convolutional neural networks. Bold values indicate the best performances.