Exploratory Data Analysis and Machine Learning Models for Stroke Prediction

Wei Fu

2023

Abstract

Stroke risk assessment is a vital area of study in healthcare. This research delves into the application of sophisticated analytical methods, combining exploratory data analysis (EDA) with advanced machine learning techniques including Random Forest, Logistic Regression, and XGBoost models. These models were deployed to predict stroke risk, leveraging key variables such as age, gender, BMI, and smoking habits. Notably, the Random Forest models exhibited robust predictive capabilities, indicating promising prospects for clinical implementation. By fusing the power of exploratory data analysis and machine learning algorithms, this study significantly enhances the early detection of stroke cases. The findings hold substantial potential for improving patient care and advancing the field of stroke risk assessment research. The integration of exploratory data analysis and machine learning not only augments the understanding of stroke risk factors but also paves the way for further scholarly investigations in this domain. The insights garnered from this research serve as a cornerstone, offering valuable direction for future studies and contributing to the continuous evolution of stroke risk assessment methodologies.

Paper Citation

in Harvard Style

Fu W. (2023). Exploratory Data Analysis and Machine Learning Models for Stroke Prediction. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 211-217. DOI: 10.5220/0012783300003885

in Bibtex Style

@conference{daml23,
author={Wei Fu},
title={Exploratory Data Analysis and Machine Learning Models for Stroke Prediction},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={211-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012783300003885},
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 - Exploratory Data Analysis and Machine Learning Models for Stroke Prediction
SN - 978-989-758-705-4
AU - Fu W.
PY - 2023
SP - 211
EP - 217
DO - 10.5220/0012783300003885
PB - SciTePress