A Novel Hybrid Ensemble Architecture for Stroke Risk Prediction Using Healthcare Data

Title

A Novel Hybrid Ensemble Architecture for Stroke Risk Prediction Using Healthcare Data

Creator

Ghosh, Tushar; George, Jossy; Chanti, S.

Description

Stroke is the reason for an alarming number of disabilities worldwide, further emphasising the critical need for early and accurate prediction of risks to inform clinical management. This paper presents a novel hybrid ensemble architecture that leverages the superiority of multiple machine learning models for stroke health risk prediction using health data. In this novel hybridisation, decision tree classifiers belonging to the Random Forest and XGBoost families are effectively combined with support vector machines and a shallow neural network within a Stacked ensemble strategy that uses a hard vote technique. To improve model generalizability and avoid overfitting, feature selection and dimensionality reduction methods like Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) have been included expertly without compromising performance. After extensive training and testing on a real-world health repository covering a broad range of demographic, lifestyle, and clinical features, the model obtained an outstanding F1-score of 0.9427 and an exemplary ROC-AUC value of 0.9872, much higher than the performance of the individual models. Statistical significance was assessed using the Friedman and Wilcoxon signed-rank test. The model is a strong candidate for incorporation into clinical decision support systems and is fully deployable and EHR-compatible. The Author(s) 2026.

Source

Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.44-55

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; Ensemble Learning; Feature Selection; Healthcare Analytics; Hybrid Models; Machine Learning; PCA; Stacking; Stroke Prediction

Coverage

Ghosh T., Christ (Deemed to Be University), Bangalore, India; George J., Christ (Deemed to Be University), Bangalore, India; Chanti S., Christ (Deemed to Be University), Bangalore, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 23673370; ISBN: 978-303222910-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-22911-3_4

https://www.scopus.com/pages/publications/105039307403?origin=resultslist