Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

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 d

SUBMITTER: Orfanoudaki A 

PROVIDER: S-EPMC7241753 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature