Fig. 5: Heatmap of feature importance analyses of XGBoost models with ECG measurements, age, and sex. | npj Digital Medicine

Fig. 5: Heatmap of feature importance analyses of XGBoost models with ECG measurements, age, and sex.

From: Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

Fig. 5

Information gain-based feature importance for various cardiovascular conditions with XGBoost models based on ECG measurements showed substantial information gain with P-duration for prediction of AF, heart rate for SVT, RR interval for UA etc. ECG electrocardiogram. Abbreviations for ECG measurements and diseases are provided in Supplementary Tables 8 and 9.

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