# Identifying predictors of stroke in young adults: a machine learning analysis of sex-specific risk factors

## TL;DR

This study uses machine learning to identify sex-specific risk factors for stroke in young adults, finding differences in behavioral and lifestyle predictors between men and women.

## Contribution

The novel aspect is the use of machine learning to explore sex-specific stroke risk factors in young adults using nationally representative data.

## Key findings

- LASSO identified marijuana use, health services, and mental health as predictors for stroke in young women.
- For men, income, heart disease, and anxiety were significant predictors of stroke.
- Tailored interventions are needed due to sex-specific differences in stroke risk factors.

## Abstract

Stroke among Americans under age 49 is increasing. While the risk factors for stroke among older adults are well-established, evidence on stroke causes in young adults remains limited. This study used machine learning techniques to explore the predictors of stroke in young men and women.

The least absolute shrinkage and selection operator algorithm (LASSO) was applied to data from Wave V of the National Longitudinal Survey of Adolescent to Adult Health (N = 12,300)—nationally representative, longitudinal panel containing demographic, lifestyle, and clinical information for individuals aged 33–43—to identify the key factors associated with stroke in men and women. The resulting LASSO model was tested and validated on an independent sample and model performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration. For robustness, synthetic minority over sampling technique (SMOTE) was applied to address data imbalance and analyses were repeated on the balanced sample.

Approximately 1.1% (N = 59) and 1.3% (N = 90) of the 5,318 and 6,970 men and women in the sample reported having a stroke. LASSO was used to predict stroke using demographic, lifestyle, and clinical predictors on both balanced and imbalanced data sets. LASSO performed slightly better on the balanced data set for women compared to the unbalanced set (Female AUC: 0.835 vs. 0.842), but performance for men was nearly identical (Male AUC: 0.820 vs. 0.822). Predictor identification was similar across both sets. For females, marijuana use, receipt of health services, education, self-rated health status, kidney disease, migraines, diabetes, depression, and PTSD were predictors. Among males, income, kidney disease, heart disease, diabetes, PTSD, and anxiety were risk factors.

This study showed similar clinical risk factors among men and women. However, variations in the behavioral and lifestyle determinants between sexes highlight the need for tailored interventions and public health strategies to address sex-specific stroke risk factors among young adults.