A novel ontology and machine learning driven hybrid cardiovascular clinical prognosis as a complex adaptive clinical system

Complex Adaptive Systems Modeling

Table 15 Classification setups considered for the development of machine learning driven cardiac chest pain prognostic model

Best classification setups
Risk factors and test results
Experimental setups	Selected features	Weighted classification Accuracy (%)
LR + FS	INA, AGE, ANG, SEX, MPS, YOS, NOC, HPT, PWY, ETT, CT, SMR	74.68
LR + BS	SMR, YOS, AGE, PWY, SEX, HPT, INA, CT, MPS, ANG	74.68
DT + SFFS	ANG, INA, CTT, ETT	78.63
DT + FS	ANG, INA,CT, ETT, DAB, SEX	77.84
SVM + FS	ANG, INA, CT, SEX, ETT, PWY, AGE, MPS, CHL,YOS	78.16
SVM + BS	YOS, AGE, PWY, SEX, HPT, CHL, INA, CT, MPS, ANG	78.32