Prediction of stroke disease with demographic and behavioural data using random forest algorithm

SHOBAYO, Olamilekan, ZACHARIAH, Oluwafemi, ODUSAMI, Modupe Olufunke and OGUNLEYE, Bayode (2023). Prediction of stroke disease with demographic and behavioural data using random forest algorithm. Analytics, 2 (3), 604-617.

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Abstract

Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. However, these studies pay less attention to the predictors (both demographic and behavioural). Our study considers interpretability, robustness, and generalisation as key themes for deploying algorithms in the medical domain. Based on this background, we propose the use of random forest for stroke incidence prediction. Results from our experiment showed that random forest (RF) outperformed decision tree (DT) and logistic regression (LR) with a macro F1 score of 94%. Our findings indicated age and body mass index (BMI) as the most significant predictors of stroke disease incidence.

Item Type:	Article
Identification Number:	https://doi.org/10.3390/analytics2030034
Page Range:	604-617
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	07 Aug 2023 10:51
Last Modified:	11 Oct 2023 12:47
URI:	https://shura.shu.ac.uk/id/eprint/32238

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