SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

Abstract In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our approach lies in the first-time application...

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Bibliographic Details
Main Authors: Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Seagull optimization algorithm
Random forest
Cancer classification
High dimensional data
Online Access:https://doi.org/10.1038/s41598-025-95786-1
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https://doi.org/10.1038/s41598-025-95786-1

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