STROKES NO MORE: INNOVATIVE EARLY PREDICTION USING MACHINE LEARNING

Abstract

Stroke poses a significant worldwide threat with severe health and economic implications, resulting from
disruptions in blood flow to the brain and causing neurological impairment. As the aging population increases, the
number of people at risk for stroke grows, emphasizing the urgent need for effective prediction systems. This project
is addressing the challenge of stroke by developing automated prediction algorithms. These algorithms aim to enable
early intervention, potentially saving lives by predicting strokes accurately. The precision and effectiveness of such
systems become increasingly crucial in managing the rising population at risk. The project involves a
comprehensive examination, comparing the effectiveness of a proposed machine learning technique with six wellknown
classifiers. Metrics related to both generalization capability and prediction accuracy were scrutinized to
evaluate the performance of the developed algorithm in stroke prediction. To provide transparency into the blackbox
nature of machine learning models, the study employs explainable techniques, specifically SHAP (Shapley
Additive Explanations). This method is well-established in the medical industry, offering insights into model
decision-making processes. The experimental results indicate that more intricate models outperformed simpler ones,
higher accuracy. The proposed framework, incorporating both global explainable methodology, aims to standardize
complex models. This standardization can enhance stroke care and treatment by providing valuable insights into the
decision-making process of the algorithms. It includes ensemble methods such as Categorical Boosting and Stacking
Classifier were applied, leveraging the combined predictions of multiple individual models to enhance overall
prediction accuracy. Notably, the Stacking Classifier demonstrated exceptional performance, achieving an
impressive 99% accuracy.