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Sequential Attribute Designator (SAD): A Novel Feature-Selection Framework for Pulmonary Disease Research
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Author(s): Sulekha Das (GCECT, Kolkata, India), Avijit Kumar Chaudhuri (Brainware University, India), Debanjan Paul (Techno Engineering College, Banipur, India)and Partha Ghosh (GCECT, Kolkata, India)
Copyright: 2026
Pages: 24
Source title:
Next-Generation Bioinformatics for Pulmonary Disease Research
Source Author(s)/Editor(s): Devvret Verma (Graphic Era University, India), Debasis Mitra (Graphic Era University, India), Bhavya Mudgal (Graphic Era University, India), Suraj Vitthaloo Atram (The University of Sheffield, UK)and Rokayya Sami (Taif University, Saudi Arabia)
DOI: 10.4018/979-8-3373-4923-7.ch013
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Abstract
The field of next-generation bioinformatics in pulmonary disease studies is transforming the diagnostic and treatment landscape by incorporating sophisticated algorithms for feature selection, high-dimensional data analysis, and AI-based predictive modeling. In this paper, the authors presented the Sequential Attribute Designator (SAD). This novel feature selection algorithm iteratively eliminates redundant features to reduce the feature space to the most determinative ones. SAD is an evolving form of random recursive feature elimination, which makes it different from the conventional static feature selection procedures. When used on a lung cancer dataset, SAD removed 15 features and increased classification accuracy to 94.11% as opposed to 78.8%. This evolution can not only minimize computational overhead but also enhance the creation of cost-effective, reliable, and interpretable diagnostic tools, thereby enabling timely interventions and improved patient survival rates in the face of pulmonary diseases.
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