ArticleScientific reports2025
SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning-based early survival prediction in early-onset hepatocellular carcinoma: a SEER-based multi-model comparative study.Translational cancer research · 2026Article
- Hybrid deep learning framework for accurate classification of high dimensional genomic data.Scientific reports · 2026Article
- Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models.PloS one · 2026Article
- Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh.PloS one · 2026Article
- Discriminative biomarker selection using hybrid multi-population evolutionary computation.Scientific reports · 2025Article
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Multimodal feature-optimized approaches for cancer classification using microarray gene expression analysis.Scientific reports · 2025Article
- Quantum-Inspired gravitationally guided particle swarm optimization for feature selection and classification.Scientific reports · 2025Article
- AI driven automation for enhancing sustainability efforts in CDP report analysis.Scientific reports · 2025Article
- GNNs surpass transformers in tumor medical image segmentation.Scientific reports · 2025Article
- Fusing wrist pulse and ECG data for enhanced identification of coronary heart disease and its complications.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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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 of SGA for gene selection in breast cancer diagnosis, where SGA systematically explores the feature space to identify the most informative gene subsets, thereby improving classification accuracy and reducing computational complexity. The selected features are subsequently classified using RF, known for its robustness and high accuracy in handling complex datasets. To evaluate the effectiveness of the proposed method, we compared it with other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The proposed SGA-RF combination achieved a best mean accuracy of 99.01% with 22 genes, outperforming other methods and demonstrating consistent performance across varying feature subsets. The mean accuracies ranged from 85.35 to 94.33%, highlighting a balance between feature reduction and classification accuracy. Future work will explore the integration of other nature-inspired algorithms and deep learning models to further enhance performance and clinical applicability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.