Ensemble Classifier for Stroke Prediction with Recurshive Feature Elimination-Reference-Cited by-同舟云学术

Ensemble Classifier for Stroke Prediction with Recurshive Feature Elimination

Published:2023-08-09 Issue: Volume: Page:357-364
ISSN:2456-3307
Container-title:International Journal of Scientific Research in Computer Science, Engineering and Information Technology
language:en
Short-container-title:IJSRCSEIT

Author:

Mitra Pooja¹,Degadwala Sheshang²,Vyas Dhairya³

Affiliation:

1. Research Scholar, Sarvajanik college of engineering and technology, Surat, Gujarat, India

2. Associate Professor & Head of Department, Dept. of Comp. Engineering, Sigma University, Gujarat, India

3. Research Scholar, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India

Abstract

This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). By iteratively selecting and excluding features, RFE enhances the model's predictive capacity while minimizing overfitting. The ensemble classifier, formed by combining diverse base classifiers, capitalizes on their complementary strengths to enhance overall predictive performance. Leveraging a comprehensive dataset, the proposed approach demonstrates superior stroke prediction accuracy compared to individual classifiers, underscoring its potential as an effective tool for early stroke risk assessment.

Publisher

Technoscience Academy

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference15 articles.

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