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Article

Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study

1
Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, Settsu 566-0002, Japan
2
National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan
3
Department of Cardiac Surgery, Cardiovascular Center, Cho Ray Hospital, Ho Chi Minh City 72713, Vietnam
4
Faculty of Informatics, Yamato University, 2-5-1 Katayama, Suita 564-0082, Japan
5
Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
6
Graduate School of Science Technology and Innovation, Kobe University, 1-1 Rokkodai Nada-ku, Kobe 657-8501, Japan
*
Authors to whom correspondence should be addressed.
J. Cardiovasc. Dev. Dis. 2024, 11(7), 207; https://doi.org/10.3390/jcdd11070207
Submission received: 26 April 2024 / Revised: 12 June 2024 / Accepted: 27 June 2024 / Published: 1 July 2024
(This article belongs to the Section Stroke and Cerebrovascular Disease)

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 7389 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 (LightGBM) 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.
Keywords: stroke; supervised machine learning; unsupervised machine learning; logistic regression; random forest; support vector machine (SVM); extreme gradient boost (XGBoost); light gradient boosted machine (LightGBM); k-prototype clustering; Shapley addictive explanations (SHAP) stroke; supervised machine learning; unsupervised machine learning; logistic regression; random forest; support vector machine (SVM); extreme gradient boost (XGBoost); light gradient boosted machine (LightGBM); k-prototype clustering; Shapley addictive explanations (SHAP)

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MDPI and ACS Style

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. J. Cardiovasc. Dev. Dis. 2024, 11, 207. https://doi.org/10.3390/jcdd11070207

AMA Style

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. Journal of Cardiovascular Development and Disease. 2024; 11(7):207. https://doi.org/10.3390/jcdd11070207

Chicago/Turabian Style

Vu, Thien, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Takao Inoué, Research Dawadi, and Michihiro Araki. 2024. "Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study" Journal of Cardiovascular Development and Disease 11, no. 7: 207. https://doi.org/10.3390/jcdd11070207

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