Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors

Yauhen Statsenko
* ORCID logo ,
Ekaterina Fursa
,
Vasyl Laver
,
Tatsiana Talako
,
Gillian Lylian Simiyu
,
Fatmah Al Zahmi
,
Darya Smetanina
ORCID logo ,
Klaus Neidl-Van Gorkom
,
Miklos Szolics
,
Jamal Al. Koteesh
* ,
Taleb M. Almansoori
* ,
Milos Ljubisavljevic
Version 1 : Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (16:00:02 CET)

How to cite: Statsenko, Y.; Fursa, E.; Laver, V.; Talako, T.; Simiyu, G.L.; Al Zahmi, F.; Smetanina, D.; Gorkom, K.N.; Szolics, M.; Al. Koteesh, J.; Almansoori, T.M.; Ljubisavljevic, M. Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors. Preprints 2024, 2024030943. https://doi.org/10.20944/preprints202403.0943.v1 Statsenko, Y.; Fursa, E.; Laver, V.; Talako, T.; Simiyu, G.L.; Al Zahmi, F.; Smetanina, D.; Gorkom, K.N.; Szolics, M.; Al. Koteesh, J.; Almansoori, T.M.; Ljubisavljevic, M. Machine Learning Models Predicting Incidence, Severity, and Early Outcomes of Hemorrhagic Stroke from Weather Parameters and Individual Risk Factors. Preprints 2024, 2024030943. https://doi.org/10.20944/preprints202403.0943.v1

Abstract

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Keywords

Machine learning classification model; Distributed lag nonlinear model; Hemorrhagic stroke; Weather; Ethnicity; Sex; Middle East; Harsh climate

Subject

Public Health and Healthcare, Public, Environmental and Occupational Health

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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