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Related Experiment Video

Updated: May 12, 2026

Modeling Stroke in Mice: Permanent Coagulation of the Distal Middle Cerebral Artery

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Published on: July 31, 2014

A machine learning model predicts stroke associated with blood cadmium level.

Wenwei Zuo¹, Xuelian Yang²

¹School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu Area, Shanghai, 200093, China.

Scientific Reports

|June 26, 2024

Summary

High blood cadmium levels are linked to increased stroke risk. Machine learning models effectively predict stroke using blood cadmium, highlighting its role as an environmental toxicant contributing to cardiovascular disease.

Keywords:

Cadmium exposure Interpretable Machine learning NHANES Predictive model Stroke

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Published on: November 22, 2024

Area of Science:

Environmental Health
Cardiovascular Disease Epidemiology
Biomedical Data Science

Background:

Stroke is a primary global cause of mortality and long-term disability.
Cadmium, an environmental toxicant, is implicated in cardiovascular disease, including stroke.
Effective identification of stroke risk factors is crucial for public health.

Purpose of the Study:

To develop and validate a machine learning (ML) model for stroke identification using blood cadmium levels.
To assess the association between blood cadmium and stroke risk in a US adult population.
To interpret the predictive contribution of blood cadmium to stroke using explainable AI methods.

Main Methods:

Utilized data from the National Health and Nutrition Examination Survey (NHANES, 2013-2014) with 2664 participants.
Employed multivariate logistic regression and five ML algorithms (KNN, DT, LR, MLP, RF) for model construction and testing.
Applied Shapley Additive exPlanations (SHAP) for feature interpretability.

Main Results:

The logistic regression (LR) model showed the best performance in identifying stroke (AUC: 0.800, accuracy: 0.966).
Higher quartiles of blood cadmium were associated with increased odds of stroke (ORs ranging from 1.32 to 2.67).
Blood cadmium was identified as a significant predictor in the ML model, indicating a positive correlation with stroke risk.

Conclusions:

Blood cadmium levels are a notable contributor to stroke risk.
Machine learning models can effectively predict stroke risk based on blood cadmium exposure.
Findings underscore the importance of monitoring environmental cadmium exposure for cardiovascular health.