Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Early detection of lung cancer is crucial for improving patient survival and reducing mortality. However, medical datasets often face challenges like irrelevant features and class imbalance, complicating accurate predictions. This study presents a comprehensive AI-powered lung cancer classification...

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Bibliographic Details
Main Authors: Sarah Ayad, Hamdi A. Al-Jamimi, Ammar El Kheir
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Early prediction
feature engineering
lung cancer
XGBoost
Online Access:https://ieeexplore.ieee.org/document/10857335/

Internet

https://ieeexplore.ieee.org/document/10857335/

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