TABLE 3.

Comparison of the predictive value of various machine learning models and actual cf‐PWV and baPWV for survival time and outcome events using the Cox proportional hazards model.

	−2 Log Likelihood	Chi‐square	df	Sig.
Actual_baPWV	4379.680	9.918	1	0.003
Actual_cf‐PWV	5069.810	17.882	1	0.000
SVR_cf‐PWV	906.595	4.278	1	0.039
RF_cf‐PWV	917.286	5.585	1	0.018
LR_cf‐PWV	520.956	8.206	1	0.004
KNN_cf‐PWV	1027.156	4.387	1	0.036
GB_cf‐PWV	869.209	3.965	1	0.046
20%_Actual_baPWV	768.779	0.730	1	0.402
20%_Actual_cfPWV	520.671	8.625	1	0.003

Note: SVR_cf‐PWV, RF_cf‐PWV, LR_cf‐PWVKNN_cf‐PWV, and GB_cf‐PWV represent cf‐PWV prediction by different machine learning models, Actual_cf‐PWV and Actual_baPWV are the value of cf‐PWV and baPWV measured by an instrument. The dataset used for this comparison includes 20% of the actual baPWV values (20%_Actual_baPWV) and 20% of the actual cf‐PWV values (20%_Actual_cfPWV). This table summarizes the performance of five machine learning models in predicting cf‐PWV values, assessed using the Cox proportional hazards model. It lists statistical indicators such as ‐2 Log Likelihood, Chi‐square, df, and Sig.