Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Table 2 Score of the Machine Learning models obtained during 5-fold Cross Validation using reduced features

	Model	Acc^a	AUC^b	Recall	Prec.^c	F1	TT^d
LR	Logistic Regression	0.7015	0.7376	0.6186	0.7436	0.6752	2.410
LDA	Linear Discriminant Analysis	0.7025	0.7370	0.5781	0.7712	0.6605	0.008
Ada Boost	Ada Boost Classifier	0.6964	0.7347	0.6248	0.7315	0.6737	0.030
NB	Naive Bayes	0.6843	0.7305	0.5823	0.7345	0.6492	0.006
RF	Random Forest Classifier	0.6772	0.7301	0.6267	0.6980	0.6601	0.196
CatBoost	CatBoost Classifier	0.6853	0.7272	0.5800	0.7398	0.6490	0.674
XGBoost	Extreme Gradient Boosting	0.6761	0.7184	0.5900	0.7159	0.6451	0.402
QDA	Quadratic Discriminant Analysis	0.6772	0.7171	0.5701	0.7267	0.6387	0.008
ET	Extra Trees Classifier	0.6690	0.7155	0.6064	0.6947	0.6469	0.178
GBC	Gradient Boosting Classifier	0.6914	0.7147	0.5761	0.7507	0.6516	0.028
LightGBM	Light Gradient Boosting Machine	0.6843	0.7146	0.5962	0.7260	0.6541	0.258
KNN	K Neighbors Classifier	0.6569	0.7058	0.5537	0.7001	0.6162	0.422
DT	Decision Tree Classifier	0.6548	0.6522	0.5618	0.6956	0.6201	0.006
Dummy	Dummy Classifier	0.4975	0.5000	0.4000	0.1990	0.2658	0.006
SVM	SVM - Linear Kernel	0.5513	0.0000	0.9091	0.5393	0.6700	0.010
Ridge	Ridge Classifier	0.7025	0.0000	0.5781	0.7712	0.6605	0.006

ISSN: 1471-2288