Predicting Brain Stroke Risk Using Machine Learning and Neural Network: A Comprehensive Approach to Early Detection and Prevention

Abstract

Stroke remains a leading cause of mortality and long-term disability, and timely identification of high-risk individuals is essential for effective primary and secondary prevention. Recent advances in machine learning (ML) and neural networks (NNs) have shown substantial promise in modelling complex, nonlinear interactions among clinical, demographic, lifestyle and imaging-derived predictors of stroke risk, frequently surpassing conventional statistical risk scores. In this study, a comprehensive framework is proposed for predicting brain stroke risk that integrates heterogeneous data sources, advanced preprocessing, model ensembling and explainability. The approach begins with rigorous data curation, including missing-value imputation, outlier handling and feature engineering, followed by strategies for addressing severe class imbalance such as cost-sensitive learning and synthetic oversampling. A diverse set of ML models (e.g., gradient boosting, random forests, support vector machines) is benchmarked against neural architectures including multilayer perceptrons, deep autoencoder-based representations and hybrid pipelines that combine tree-based learners with NN-derived feature spaces. Model selection is performed using nested cross-validation with discrimination, calibration and decision-analytic metrics (AUROC, F1-score, Brier score and net benefit) to ensure clinically meaningful performance. To enhance trust and adoption in practice, post-hoc and intrinsic explainability tools (e.g., SHAP-based feature attributions and global importance profiles) are employed to reveal individual- and population-level drivers of predicted risk, with particular emphasis on modifiable factors (such as hypertension, diabetes, atrial fibrillation and lifestyle variables). The proposed framework is intended to support deployment-ready, interpretable risk stratification tools that can be embedded in electronic health record systems and community screening programs, thereby enabling earlier interventions and potentially reducing stroke incidence and burden across diverse populations.