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Stroke Deaths Profile and Its Subtypes in Brazil: Analysis Using Machine Learning.

Alessandro Rocha Milan de Souza1, Letícia Martins Raposo2, Glenda Corrêa Borges de Lacerda1

  • 1Graduate Program in Neurology of the Center for Biological and Health Sciences, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil.

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Summary

Machine learning identified key factors differentiating ischemic stroke (IS) from hemorrhagic stroke (HS) in Brazil. This analysis of over 2.4 million stroke deaths highlights distinct demographic and comorbidity profiles for each stroke subtype.

Keywords:
hemorrhagic strokeischemic strokemachine learningmortality

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Area of Science:

  • Public Health
  • Epidemiology
  • Medical Informatics

Background:

  • Brazil faces high stroke mortality rates, necessitating deeper understanding of contributing factors.
  • Sociodemographic influences and co-occurring conditions significantly impact stroke mortality.
  • Machine learning offers advanced analytical capabilities for stroke research.

Purpose of the Study:

  • To analyze the profile of stroke deaths and their subtypes in Brazil.
  • To utilize machine learning for identifying patterns in stroke mortality data.
  • To differentiate between ischemic stroke (IS) and hemorrhagic stroke (HS) using data-driven approaches.

Main Methods:

  • Time series analysis of Brazilian mortality data (2000-2019).
  • Classification of stroke deaths into ischemic stroke (IS), hemorrhagic stroke (HS), and unspecified stroke (US).
  • Development of a decision tree model to distinguish between IS and HS based on associated conditions and demographics.

Main Results:

  • Over 2.4 million deaths mentioned stroke, with a notable increase over time.
  • Unspecified stroke (US) constituted over 62% of cases; respiratory diseases were common comorbidities for IS and US.
  • Decision tree revealed IS is linked to older age and conditions like heart disease, while HS is more common in younger individuals with nervous system diseases.

Conclusions:

  • Machine learning effectively identified key differentiating factors between IS and HS.
  • Distinct clinical and demographic profiles aid in recognizing stroke subtypes.
  • Findings can inform clinical practice and improve classification of unspecified stroke deaths.