Exploring Stroke Risk Identification by Machine Learning: A Systematic Review

• Autores: Atencia Mondragon, Lelis Raquel, Huarcaya Carbajal, Melany Cristina, Guzmán Jiménez, Rosario Marybel
• Formato: objeto de conferencia
• Fecha de Publicación: 2024
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/20835
• Enlace del recurso: https://hdl.handle.net/20.500.12724/20835; https://doi.org/10.26439/ciis2023.7081
• Nivel de acceso: acceso abierto
• Materia: Pendiente; https://purl.org/pe-repo/ocde/ford#2.02.04

This work aims to systematize previous studies on stroke risk identification and its relationship with machine learning. A systematic review was conducted using the Web of Science and Scopus databases. The information was organized into three sections: stroke risk factors, data preprocessing techniques and techniques for identifying stroke risk with an emphasis on the most important features. The main results are as follows: risk factors are divided into modifiable (work environment and air pollution) and non-modifiable (sex, family history). The most commonly used data preprocessing techniques are SMOTE, standardization and value elimination/imputation. The most commonly used techniques for identifying stroke risk include support vector machine, random forest, logistic regression, naïve Bayes, k-nearest neighbors and decision tree.