Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score
Name
pone.0232414.pdf
Description
Published version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
b9f18e4a52f2d2ccedf847f2d1bde092
Author(s)
Orfanoudaki, Agni
Chesley, Emma
Cadisch, Christian
Bertsimas, Dimitris J
Date Issued
May 2020
Journal
PLoS ONE
Publisher
Public Library of Science (PLoS)
Citation
Orfanoudaki, Agni et al. “Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.” PLoS ONE, 15, 5 (May 2020): e0232414 © 2020 The Author(s)
Version
Final published version
Abstract
Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85–0.90) vs. 73.74% (CI 0.70–0.76); validation 75.29% (CI 0.74–0.76) vs 65.93% (CI 0.64–0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PONE.0232414
