Prediction of the Probability of Stroke Based on the Random Forest

Bowen Tang

2023

Abstract

As stroke is becoming more and more popular worldwide range among elderly people, it is found that having it brings irreversible damage to the human body and also a commercial burden to families and the medical resources of a country. According to studies, about 6 million patients died after experiencing stroke and most of them had previous symptoms before having it while they ignored them. Predicting the probability of avoiding the disease comes out to be the only and the most effective solution for stroke. In this paper, the contributions of stroke will be found while figures of correlations with stroke will be produced using Python and a random forest model. The aim of this paper is to use Python to predict the probability of stroke based on a random forest model, which is necessary for preventing stroke in advance and has a chance to save millions of lives from having stroke.

Paper Citation

in Harvard Style

Tang B. (2023). Prediction of the Probability of Stroke Based on the Random Forest. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 42-46. DOI: 10.5220/0012809900003885

in Bibtex Style

@conference{daml23,
author={Bowen Tang},
title={Prediction of the Probability of Stroke Based on the Random Forest},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={42-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012809900003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Prediction of the Probability of Stroke Based on the Random Forest
SN - 978-989-758-705-4
AU - Tang B.
PY - 2023
SP - 42
EP - 46
DO - 10.5220/0012809900003885
PB - SciTePress