Prediction Model of Stroke Based on Few-Shot Learning

Xujun Wu, Guoxin Wang

2022

Abstract

Stroke has the characteristics of high morbidity, high disability rate and high mortality, and has become the first cause of death in China; timely screening of specific populations and prediction through prediction models are of great significance for disease risk control. In this paper, we use machine learning algorithm to intelligently process the stroke screening data, and reasonably expand the small sample data. On this basis, we use decision tree and Bagging algorithm to establish a stroke prediction model, and discuss the modeling process and model parameter selection in detail. The test results show that the prediction model based on the extended data set runs well on the test data set, and this method provides a reference for Few-shot learning modeling.

Paper Citation

in Harvard Style

Wu X. and Wang G. (2022). Prediction Model of Stroke Based on Few-Shot Learning. In Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME; ISBN 978-989-758-636-1, SciTePress, pages 694-698. DOI: 10.5220/0012042100003620

in Bibtex Style

@conference{icemme22,
author={Xujun Wu and Guoxin Wang},
title={Prediction Model of Stroke Based on Few-Shot Learning},
booktitle={Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME},
year={2022},
pages={694-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012042100003620},
isbn={978-989-758-636-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME
TI - Prediction Model of Stroke Based on Few-Shot Learning
SN - 978-989-758-636-1
AU - Wu X.
AU - Wang G.
PY - 2022
SP - 694
EP - 698
DO - 10.5220/0012042100003620
PB - SciTePress