Application of Data Mining and Machine Learning Techniques in the Comparison of Stroke Profiles Between Elderly and Middle-Aged Adults: Study of the 2019 PNS

• Ligia Ferreira de Carvalho Gonçalves Pontifical Catholic University of Minas Gerais http://orcid.org/0009-0000-7601-2938
• Luis Enrique Zárate Gálvez Pontifical Catholic University of Minas Gerais

DOI: https://doi.org/10.5753/sbbd_estendido.2024.243520

Abstract

This work aims to explore the use of machine learning techniques to describe the profile of individuals diagnosed with stroke, in order to compare the profile between two distinct age groups: middle-aged adults (40-59) and elderly individuals (60-80). The Decision Tree algorithm was applied to the database provided by the 2019 Brazilian National Health Survey. The conclusions indicate that the rules generated for middle-aged adults are mainly about routine habits, such as work or salt consumption, while for elderly individuals they are more related to intrinsic factors, such as the presence of chronic diseases or gender.

Keywords: Data Mining, Machine Learning, Stroke

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