Stroke risk prediction using multiple machine learning algorithms

Wentao Zhong; Chuyuan Zhou; Danni Zhou

doi:10.1117/12.2673750

14 April 2023 Stroke risk prediction using multiple machine learning algorithms

Wentao Zhong, Chuyuan Zhou, Danni Zhou

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 1261311 (2023) https://doi.org/10.1117/12.2673750
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Stroke is an acute cerebrovascular disease with high morbidity, mortality, and disability rate. The survivors could suffer from long-term motor impairment. Due to the persistent lack of effective treatments, prevention is currently considered the best measure. In this study, two objectives are investigated. The former attempts to analyze which characteristics would cause people prone to have a stroke. The latter explores machine learning algorithms with satisfactory performances for stroke risk prediction. To achieve this goal, five machine learning algorithms are validated, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). Afterward, an ensemble learning algorithm, namely weighed voting algorithm is implemented to further improve performances. Various metrics are leveraged to comprehensively evaluate the results, including accuracy, precision, recall, F1-score, and the Area Under Curve (AUC). Finally, an acceptable result is achieved with an AUC of 0.711 and a recall of 57.9%. This work demonstrates that the ensemble learning method could improve performances and be further exploited as a reliable classifier for stroke prediction.

Citation Download Citation

Wentao Zhong, Chuyuan Zhou, and Danni Zhou "Stroke risk prediction using multiple machine learning algorithms", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 1261311 (14 April 2023); https://doi.org/10.1117/12.2673750

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KEYWORDS

Machine learning

Data modeling

Binary data

Education and training

Decision trees

Performance modeling

Random forests

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