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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,

Published byΞάνθη Μαγγίνας Modified over 5 years ago

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Presentation on theme: "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,"— Presentation transcript:

1 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 e3 (November 2016) DOI: /j.jvs Copyright © 2016 Society for Vascular Surgery Terms and Conditions

2 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  , e3DOI: ( /j.jvs ) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

3 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  , e3DOI: ( /j.jvs ) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

4 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  , e3DOI: ( /j.jvs ) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

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

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

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

8 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  , e3DOI: ( /j.jvs ) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

9 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  , e3DOI: ( /j.jvs ) Copyright © 2016 Society for Vascular Surgery Terms and Conditions

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

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

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