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Review
. 2019 May;12(5):e004879.
doi: 10.1161/CIRCOUTCOMES.118.004879.

Tree-Based Analysis

Mousumi Banerjee  1   2 , Evan Reynolds  1 , Hedvig B Andersson  3   2   4 , Brahmajee K Nallamothu  3   2
Affiliations
Review

Tree-Based Analysis

Mousumi Banerjee et al. Circ Cardiovasc Qual Outcomes. 2019 May.

Erratum in

Abstract

Tree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. Tree-based methods provide a natural framework for creating patient subgroups for risk classification. In this article, we review methodological and practical aspects of tree-based methods, with a focus on diagnostic classification (binary outcome) and prognostication (censored survival outcome). Creating an ensemble of trees improves prediction accuracy and addresses instability in a single tree. Ensemble methods are described that rely on resampling from the original data. Finally, we present methods to identify a representative tree from the ensemble that can be used for clinical decision-making. The methods are illustrated using data on ischemic heart disease classification, and data from the SPRINT trial (Systolic Blood Pressure Intervention Trial) on adverse events in patients with high blood pressure.

Keywords: classification; clinical decision-making; coronary artery disease; hypertension; risk.

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Figures

Figure 1:
Figure 1:
Single Tree Analysis for Heart Disease Classification.
Figure 2:
Figure 2:
Variable Importance from Random Forest for Heart Disease Classification.
Figure 3:
Figure 3:
Representative Tree from Random Forest for Heart Disease Classification.
Figure 4:
Figure 4:
Single Tree Analysis for SPRINT data.
Figure 5:
Figure 5:
Variable Importance from Random Forest for SPRINT data.
Figure 6:
Figure 6:
Representative Tree from Random Forest for SPRINT data.
See this image and copyright information in PMC

References

    1. Goldman L, Weinberg M, Weisberg M, Olshen R, Cook F, Sargent RK, Lamas GA, Dennis C, Wilson C, Deckelbaum L, Fineberg H, Stiratelli R. A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. The New England Journal of Medicine. 1982; 307:588–596. - PubMed
    1. Mortazavi B, Downing N, Bucholz E, Dharmarajan K, Manhapra A, Li S, Negahban S, Krumholz H. Analysis of machine learning techniques for heart failure readmissions. Circulation Cardiovascular Quality and Outcomes. 2016; 9:629–640. - PMC - PubMed
    1. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. 1984. Belmont, California: Wadsworth.
    1. Banerjee M, George J, Song EY, Roy A, Hryniuk W. Tree-based model for breast cancer prognostication. Journal of Clinical Oncology. 2004; 22:2567–2575. - PubMed
    1. Therneau TM, Grambsch PM, Fleming TR. Martingale-based residuals for survival models. Biometrika. 1990;77:147–160.

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