Table 3. Performance of the proposed architectures in terms of area under the Precision–Recall curve.

Subset	Baseline	Semi	Sym	Zero	SVM	k-NN	DecTree
	0.1334	0.1424	0.1534	0.1744	0.0877	0.0345	0.1941
C	0.1998	0.1853	0.2115	0.2174	0.1550	0.3033	0.2560
H	0.4536	0.4459	0.4407	0.4625	0.4192	0.3885	0.3616
S	0.6416	0.6411	0.6335	0.6668	0.5905	0.5681	0.6242
CH	0.4752	0.4684	0.4754	0.4609	0.4423	0.4095	0.4023
CS	0.6265	0.6424	0.6388	0.5985	0.6205	0.5379	0.6089
HS	0.6200	0.6356	0.6277	0.5864	0.6199	0.6335	0.5956
CHS	0.6665	0.6351	0.6875	0.6404	0.6374	0.6653	0.5542
Best	0	2	2	2	0	1	1

Notes:

The subset of observable screening strategies include: Cytology (C), Hinselmann (H), and Schiller (S).

Baseline, deep feed-forward neural network; Semi, semi-supervised dimensionality reduction (Eq. 2); Sym, symmetry mapping dimensionality reduction (Eq. 4); Zero, zero mapping dimensionality reduction (Eq. 5); SVM, support vector machine; k-NN, k-nearest neighbors; DecTree, decision tree.

We highlight the best performing models in bold.