Key result
XGBoost model using routine blood tests predicts cerebral infarction in healthy individuals with ~1.00 AUC.
Why the study?
Diagnosing cerebral infarction and TIA is challenging in resource-limited settings lacking advanced imaging and specialized expertise, motivating a simple, convenient, blood-based diagnostic approach.
Can machine learning models using routine blood tests accurately predict cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism?
Observational (n=36,269)
No
Can machine learning models using routine blood tests accurately predict cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism?
Effect estimate: AUC 0.9958 (95% CI 0.9947-0.9969)
Machine learning models utilizing routine blood tests and basic demographics can accurately predict cerebral infarction and TIA, potentially serving as a low-cost diagnostic adjunct in resource-limited settings.
May support accessible prediction tools using routine labs; leaves open prospective validation before clinical use.
Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, hypercholesterolemia, and diabetes, are at increased risk for cerebral infarction (CI) and TIA. In resource-limited settings, diagnosing CI and TIA can be particularly difficult due to a lack of advanced imaging and specialized expertise. Therefore, we aim to develop a simple, convenient, blood-based approach that could assist clinicians in diagnosing CI and TIA, especially in regions where advanced imaging or stroke-specific expertise is limited. All study subjects were patients admitted to the First Hospital of Xiamen University and healthy check-up populations between January 2018 and September 2023. This study employed five machine learning methods alongside 21 blood routine indicators, 30 blood biochemical indicators, age, and gender to construct predictive models for CI and TIA in both healthy individuals and those with dysmetabolism. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) served as the metric to assess the comprehensive predictive capability of the models. Subsequently, the SHAP package was employed for model interpretation. Extreme Gradient Boosting (XGBoost) outperforms other models in all predictive models. In the models predicting CI and TIA among healthy, the AUC is 0.9958 (0.9947-0.9969) and 0.9928 (0.9899-0.9951), respectively. Among the nine shared key features of the two models are indicators of glucose metabolism, lipid metabolism, and liver metabolism. In the models for predicting CI and TIA among patients with hypertension, hypercholesterolemia, diabetes, and those with all three metabolic disorders combined, the AUCs ranged from 0.6990 to 0.8591. We found that the indicators K significantly contributed to predict CI and TIA from those with dysmetabolism. Additionally, metabolic-related indicators, such as glucose (GLU) and high-density lipoprotein cholesterol (HDL-C), are ranked highly among the top ten contributing features. XGBoost performed the best in all models. It can effectively differentiate CI and TIA from healthy and dysmetabolic patients by combining blood routine and blood biochemical indicators. Moreover, it can also differentiate CI from TIA. Although any suspicious findings from this model would still require confirmatory imaging, the simplicity and low cost of blood-based testing may offer a practical adjunct for clinicians-particularly in areas lacking advanced imaging or extensive stroke expertise-and could facilitate earlier diagnostic decision-making.
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Yang et al. (2025) conducted an observational in Cerebral infarction and transient ischemic attack (n=36,269). Extreme Gradient Boosting (XGBoost) machine learning model vs. Other machine learning models (SVM, Random Forest, Logistic Regression, DNN) was evaluated on Prediction of cerebral infarction among healthy individuals (AUC 0.9958, 95% CI 0.9947-0.9969). An Extreme Gradient Boosting (XGBoost) machine learning model combined with routine blood tests accurately predicted cerebral infarction among healthy individuals with an AUC of 0.9958.
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