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Ischemic stroke prediction using machine learning in elderly Chinese population: The Rugao Longitudinal Ageing Study
Huai-Wen Chang1, Hui Zhang2,3, Guo-Ping Shi2,4
1Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.
Brain and Behavior
|November 7, 2023
Summary
Machine learning (ML) models, particularly Support Vector Machines (SVM), demonstrated superior accuracy in predicting ischemic stroke risk in China
Area of Science:
- Gerontology
- Neurology
- Biostatistics
Background:
- Ischemic stroke poses a significant health risk to the elderly population.
- Accurate risk prediction is crucial for timely intervention and prevention strategies.
- Traditional logistic regression (LR) models may not fully capture complex risk factor interactions.
Purpose of the Study:
- To compare the predictive performance of logistic regression (LR) with various machine learning (ML) models for ischemic stroke risk in elderly Chinese individuals.
- To identify key predictors of ischemic stroke in this demographic.
- To evaluate the potential of ML in enhancing clinical risk assessment.
Main Methods:
- Utilized data from 2208 participants in the Rugao Longitudinal Ageing Study (RLAS).
- Input variables included 103 phenotypes.
- Compared LR with ML models: Random Forest (RF), Gaussian kernel Support Vector Machines (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbors Algorithm (KNN), and Gradient Boosting Decision Tree (GBDT) for 3-year ischemic stroke risk prediction.
Main Results:
- Identified age, pulse, waist circumference, education, β2-microglobulin, homocysteine, cystatin C, folate, free triiodothyronine, platelet distribution width, QT interval, and QTc interval as significant predictors.
- ML models, especially SVM, effectively incorporated multidimensional phenotypic indicators (biochemical, ECG) beyond demographic factors used by LR.
- SVM achieved the best discrimination and calibration (C-index: 0.79), showing an 11.27% improvement in model utility over LR.
Conclusions:
- Machine learning models, particularly SVM, offer superior accuracy and better discrimination/calibration for ischemic stroke risk prediction in elderly Chinese populations compared to logistic regression.
- ML's ability to integrate diverse phenotypic data enhances predictive power.
- These findings highlight the clinical utility and potential of ML in improving ischemic stroke risk assessment and management.
