Version 1
: Received: 14 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (13:25:32 CEST)
How to cite:
Vu, T.; Kokubo, Y.; Inoue, M.; Yamamoto, M.; Mohsen, A.; Martin-Morales, A.; Inoué, T.; Dawadi, R.; Araki, M. Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. Preprints2024, 2024050975. https://doi.org/10.20944/preprints202405.0975.v1
Vu, T.; Kokubo, Y.; Inoue, M.; Yamamoto, M.; Mohsen, A.; Martin-Morales, A.; Inoué, T.; Dawadi, R.; Araki, M. Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. Preprints 2024, 2024050975. https://doi.org/10.20944/preprints202405.0975.v1
Vu, T.; Kokubo, Y.; Inoue, M.; Yamamoto, M.; Mohsen, A.; Martin-Morales, A.; Inoué, T.; Dawadi, R.; Araki, M. Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. Preprints2024, 2024050975. https://doi.org/10.20944/preprints202405.0975.v1
APA Style
Vu, T., Kokubo, Y., Inoue, M., Yamamoto, M., Mohsen, A., Martin-Morales, A., Inoué, T., Dawadi, R., & Araki, M. (2024). Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study. Preprints. https://doi.org/10.20944/preprints202405.0975.v1
Chicago/Turabian Style
Vu, T., Research Dawadi and Michihiro Araki. 2024 "Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study" Preprints. https://doi.org/10.20944/preprints202405.0975.v1
Abstract
Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7,389 participants and 53 variables. Initially, unsupervised K-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (Light-GBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised K-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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