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Fig. 5 | BMC Medical Informatics and Decision Making

Fig. 5

From: Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients

Fig. 5

Feature importance plot in the XGBoost model

The top 15 clinical variables are shown. The yellow and purple points in each row represent participants having low to high values of the specific variable, while the x-axis gives the SHAP value which affects the model [i.e. does it tend to drive the predictions towards the event (positive value of SHAP) or non-event (negative value of SHAP)]

CACS, coronary artery calcium score; XGBoost, extreme gradient boosting; SHAP, Shapley additive explanation values; E/e’, early diastolic transmitral velocity to early mitral annulus diastolic velocity ratio; Carotid artery IMT, Carotid artery intima-media thickness

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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