A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Table 2

Evaluation of prediction results from ML-kNN, kNN, DT, and SVM using physicochemical indexes (PI).

Evaluation criteria	ML-kNN	kNN	DT	SVM
Average precision	0.714 ± 0.024	0.497 ± 0.028	0.488 ± 0.036	0.554 ± 0.039
Hamming loss	0.233 ± 0.021	0.297 ± 0.030	0.308 ± 0.020	0.236 ± 0.028
Ranking loss	0.169 ± 0.012	0.698 ± 0.053	0.678 ± 0.044	0.706 ± 0.046
Coverage	5.123 ± 0.476	7.512 ± 0.894	7.866 ± 0.796	7.648 ± 0.743

Representing the index in this model is the best compared with others.