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Forecasting extremely high ischemic stroke incidence using meteorological time serie.

Lucia Babalova1, Marian Grendar2,3, Egon Kurca1

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Predicting extremely high stroke incidence using weather data proved challenging. Meteorological factors did not significantly improve forecasting accuracy for ischemic strokes, indicating limited predictive power for these events.

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Area of Science:

  • Neurology
  • Epidemiology
  • Meteorology
  • Data Science

Background:

  • Inconsistent findings exist regarding the link between weather conditions and stroke incidence.
  • Predictive modeling for stroke incidence based on meteorological factors is infrequent.
  • This study investigates extreme ischemic stroke incidence and meteorological factors in Slovakia.

Purpose of the Study:

  • To explore the relationship between extreme ischemic stroke incidence and meteorological factors.
  • To construct and evaluate forecasting models for predicting days with extremely high stroke numbers.

Main Methods:

  • Analyzed 52,036 ischemic stroke cases over five years (2015-2019).
  • Identified extreme stroke days using the 90th percentile of historical records.
  • Employed Logistic Regression, Random Forest for Time Series, and Croston's method for one-day-ahead forecasting.

Main Results:

  • Cross-correlations between extreme stroke numbers and meteorological factors were negligible.
  • Forecasting accuracy of logistic regression and Random Forest was comparable to Croston's method.
  • All three forecasting methods demonstrated limited predictive accuracy for extreme stroke events.

Conclusions:

  • Predicting days with exceptionally high stroke incidence is challenging.
  • Incorporating meteorological parameters did not substantially enhance forecasting accuracy.
  • Further research may be needed to identify reliable predictors for extreme stroke events.