The use of machine learning for the identification of peripheral artery disease and future mortality risk Elsie Gyang Ross, MD, MSc, Nigam H. Shah, MBBS,

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The use of machine learning for the identification of peripheral artery disease and future mortality risk Elsie Gyang Ross, MD, MSc, Nigam H. Shah, MBBS, PhD, Ronald L. Dalman, MD, Kevin T. Nead, MD, MPhil, John P. Cooke, MD, PhD, Nicholas J. Leeper, MD Journal of Vascular Surgery Volume 64, Issue 5, Pages 1515-1522.e3 (November 2016) DOI: 10.1016/j.jvs.2016.04.026 Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Fig 1 Area under the curve (AUC) for stepwise logistic regression and machine-learned penalized regression model for identification of patients with peripheral artery disease (PAD). Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Fig 2 Top 20 weighted variables used in the machine-learned penalized regression model for identification of patients with peripheral artery disease (PAD). Ace, Angiotensin-converting enzyme. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Fig 3 Area under the curve (AUC) for stepwise logistic regression and machine-learned random forest model for prediction of mortality. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Fig 4 Top 20 weighted variables used in the machine-learned random forest model for mortality prediction. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 1 (online only) Calibration plot for machine-learned penalized regression model for peripheral artery disease (PAD) classification. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 2 (online only) Calibration plot for stepwise logistic regression model for peripheral artery disease (PAD) classification. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 3 (online only) Area under the curve (AUC) for stepwise logistic regression and machine-learned random forest model for identification of patients with undiagnosed peripheral artery disease (PAD). Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 4 (online only) Top 20 weighted variables used in machine-learned random forest model for identification of patients with undiagnosed peripheral artery disease (PAD). HDL, High-density lipoprotein; WIQ, Walking Impairment Questionnaire. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 5 (online only) Calibration plot for machine-learned random forest model for mortality prediction. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

Supplementary Fig 6 (online only) Calibration plot for stepwise logistic regression model for mortality prediction. Journal of Vascular Surgery 2016 64, 1515-1522.e3DOI: (10.1016/j.jvs.2016.04.026) Copyright © 2016 Society for Vascular Surgery Terms and Conditions