Comparative Analysis of Machine Learning Models for Stroke Risk Prediction

Ziqian Gao

2024

Abstract

As the volume of medical data continues to grow rapidly, machine learning technologies have shown great promise in predicting the risk of stroke. Stroke remains a leading cause of disability and death worldwide, highlighting the importance of early and accurate risk prediction for effective prevention and management. This study aims to enhance stroke risk prediction by systematically evaluating the performance of various machine learning models, including Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Classifier, and Support Vector Machines. The study systematically compares these models based on metrics such as accuracy, precision, recall, F1-score, and ROC-AUC values obtained from a well-preprocessed dataset. The results show that the Random Forest model outperformed the others, demonstrating higher accuracy and robustness, indicating its potential usefulness in clinical settings for early prediction of stroke risk. Future studies could explore more advanced data analysis techniques and consider incorporating newer models like neural networks to further enhance predictive performance.

Paper Citation

in Harvard Style

Gao Z. (2024). Comparative Analysis of Machine Learning Models for Stroke Risk Prediction. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 93-101. DOI: 10.5220/0012998400004601

in Bibtex Style

@conference{iampa24,
author={Ziqian Gao},
title={Comparative Analysis of Machine Learning Models for Stroke Risk Prediction},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={93-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012998400004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Comparative Analysis of Machine Learning Models for Stroke Risk Prediction
SN - 978-989-758-722-1
AU - Gao Z.
PY - 2024
SP - 93
EP - 101
DO - 10.5220/0012998400004601
PB - SciTePress