A data-driven approach to predicting diabetes and cardiovascular disease with machine learning

Table 6 Results using 10-fold cross-validation for cardiovascular disease classification

Lab	Year	Model	AUC	Precision	Recall	F1
No lab		Logistic Reg.	0.822	0.74	0.74	0.74
	2007-2014	SVM	0.816	0.74	0.74	0.74
		Random Forest	0.829	0.75	0.74	0.74
		XGBoost	0.830	0.74	0.74	0.74
		Ensemble	0.831	0.75	0.75	0.75
With lab		Logistic Reg.	0.827	0.75	0.75	0.75
	2007-2014	SVM	0.825	0.75	0.75	0.75
		Random Forest	0.836	0.76	0.76	0.76
		XGBoost	0.838	0.76	0.76	0.76
		Ensemble	0.839	0.76	0.76	0.76

Lab - Laboratory results, AUC - Area Under the Curve, \(Precision = \frac {TP}{TP + FP}, Recall = \frac {TP}{TP + FN}\) (where TP - True Positive, FP - False Positive, FN - False Negative), and F1 (score) = \(2\frac {precision*recall}{precision + recall}\). Bold face font signifies best performing model result

ISSN: 1472-6947