Table 10. Time performance comparison of ACS prediction models and FS method.

Index	Model	Original time (s)	Time after FS (s)	Improvement (%)
1	Logistic Regression	0.057(±0.0057)	0.030(±0.0022)	47.5%
2	Random Forest	0.770(±0.0343)	0.461(±0.0134)	40.1%
3	Gradient Boosting	1.730(±0.0158)	0.515(±0.0200)	70.2%
4	XGBoost	0.306(±0.0452)	0.144(±0.0101)	53.1%
5	Deep Neural Network	13.855(±0.4814)	13.502(±0.1782)	5.2%
6	1D-CNN	128.373(±3.6955)	70.287(±1.0336)	45.2%
Index	FS Method		Processing time (s)
7	RFE		7.211(±0.3250)
8	RFECV		68.607(±0.9883)
9	XGboost		25.640(±0.2424)
10	Proposed		38.823(±0.3643)