Genetic instance-based learner algorithm for lymphography cancer diagnosis
Lymphography involvement is a fundamental indicator of cancer progression, making the precise classification of lymphographic findings essential for reliable diagnosis and treatment decision-making. This study presents an embedded approach for lymphography cancer detection that synergistically combines a genetic algorithm (GA) and an instance-based learning classifier. Unlike prior GA hybrids, this approach incorporates GA feature selection with K*, a relative-entropy-based approach that handles noisy, small-sample-size, class-imbalance, and redundant features in lymphography data, rather than the traditional hybrid approach that used KNN with a geometric distance metric (typically Euclidean or Manhattan). Although vital for diagnosing lymphatic diseases, lymphography poses challenges because of the data it generates. To address these challenges, the proposed approach integrates the strengths of GA to optimize the most important features. Instance-based learning (i.e., K* classifier) is used for effective pattern recognition and to classify lymph nodes as benign, normal, metastatic, malignant, or fibrotic. Extensive experimental results on a real-world dataset demonstrated that the combination significantly outperformed state-of-the-art classification algorithms and other hybrid approaches. The efficiency of the proposed approach is evaluated using the most widely used computational metrics, yielding promising results for diagnosing lymphatic diseases. This work highlights the potential of an evolutionary-based embedded classifier for classifying complex biomedical data.
Abdollahi, J., & Aref, S. (2024). Early prediction of diabetes using feature selection and machine learning algorithms. SN Computer Science, 5(2), 217. https://doi.org/10.1007/s42979-023-02545-y
Abdollahi, J., & Nouri-Moghaddam, B. (2022). Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran Journal of Computer Science, 5(3), 205–220. https://doi.org/10.1007/s42044-022-00100-1
Abdolrazzagh-Nezhad, M., & Izadpanah, S. (2025). A new hybrid fuzzy bio-inspired classifier for cancer detection using cuckoo optimization and hyper-planes. Data Technologies and Applications, 59(3), 416–451. https://doi.org/10.1108/DTA-06-2024-0647
Ali, W., & Saeed, F. (2023). Hybrid filter and genetic algorithm-based feature selection for improving cancer classification in high-dimensional microarray data. Processes, 11(2), 562. https://doi.org/10.3390/pr11020562
Amini, F., & Hu, G. (2021). A two-layer feature selection method using genetic algorithm and elastic net. Expert Systems with Applications, 166, 114072. https://doi.org/10.1016/j.eswa.2020.114072
Babichev, S., Liakh, I., & Skvor, J. (2025). Integrating data mining, deep learning, and gene ontology analysis for gene expression-based disease diagnosis systems. IEEE Access, 13, 21265–21278. https://doi.org/10.1109/ACCESS.2025.3535999
Cheng, C. H., & Shi, S. (2025). Artificial intelligence in cancer: Applications, challenges, and future perspectives. Molecular Cancer, 24(1), 274. https://doi.org/10.1186/s12943-025-02450-3
Islam, T., Hoque, E., Ullah, M., Islam, T., Akter Nishu, N., & Islam, R. (2024). CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma. Cancer Medicine, 13(16), e70069. https://doi.org/10.1002/cam4.70069
Lei, P., Fraser, C., Jones, D., Ubellacker, J. M., & Padera, T. P. (2024). Lymphatic system regulation of anti-cancer immunity and metastasis. Frontiers in Immunology, 15, 1449291. https://doi.org/10.3389/fimmu.2024.1449291
Liu, X., Liang, Y., Sai, W., Yang, Z. Y., & Ye, H. S. (2018). A hybrid genetic algorithm with wrapper-embedded approaches for feature selection. IEEE Access, 6(1), 22863–22874. https://doi.org/10.1109/ACCESS.2018.2818682
Mahmoud, A., & Takaoka, E. (2025). An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data. Healthcare Analytics, 7, 100373. https://doi.org/10.1016/j.health.2024.100373
Maurya, S. P., Sisodia, P. S., Mishra, R., & Pratap, D. (2024). Performance of machine learning algorithms for lung cancer prediction: A comparative approach. Scientific Reports, 14(1), 18562. https://doi.org/10.1038/s41598-024-58345-8
Negm, A. S., Collins, J. D., Bendel, E. C., Takahashi, E., Koepsel, E. M. K., Gehling, K. J., ... & Thompson, S. M. (2024). MR lymphangiography in lymphatic disorders: Clinical applications, institutional experience, and practice development. Radiographics, 44(2), e230075. https://doi.org/10.1148/rg.230075
Rabie, A. H., Aldawsari, M., Saleh, A. I., Saraya, M., & Rashad, M. (2025). HFSA: Hybrid feature selection approach to improve medical diagnostic system. PeerJ Computer Science, 11(1), e2764. https://doi.org/10.7717/peerj-cs.2764
Raza, R., Wang, G., Wong, K. W., Laga, H., & Fisichella, M. (2025). ITL-LIME: Instance-based transfer learning for enhancing local explanations in low-resource data settings. Proceedings of the 34th ACM International Conference on Information and Knowledge Management, November 2025, 2482–2492. https://doi.org/10.1145/3746252.3761183
Saqib, S. M., Muhammad, O., Mazhar, T., Iqbal, M., Guizani, S., & Hamam, H. (2025). Medicine image classification using deep learning: Highlighting the MedNet‑MoBiL hybrid model. Discover Computing, 28(1), 103. https://doi.org/10.1007/s10791-025-09619-w
Song, Y. W., Wang, J. S., Qi, Y. L., Wang, Y. C., Li, S., & Song, H. M. (2025). PF‑PSS: A double‑layer parallel embedded feature selection method for cancer gene expression data. Journal of Big Data, 12(1), 136. https://doi.org/10.1186/s40537-025-01144-3
Song, Y. W., Wang, J. S., Qi, Y. L., Wang, Y. C., Song, H. M., & Shang-Guan, Y. P. (2025). Serial filter-wrapper feature selection method with elite guided mutation strategy on cancer gene expression data. Artificial Intelligence Review, 58(4), 111. https://doi.org/10.1007/s10462-024-11029-1
Tabasi, S., Ahanian, I., Amiri, N., Dabanloo, N. J., & Barghamadi, H. (2025). Deep learning based cancer classification using selected gene expression data. Signal Processing and Renewable Energy, 9(3), 1–10. https://doi.org/10.57647/j.spre.2025.090318
Thambi, S. V., G, I., Kammari, K. S., & Sahay, A. (2025). Hybrid metaheuristic optimisation for lung cancer image classification: Leveraging MOEA, PSO, and ACO algorithms. Procedia Computer Science, 258(1), 3781–3793. https://doi.org/10.1016/j.procs.2025.04.633
Valeriano, M. G., Marzag, D. K., Montelongo, A., Roberto, C., Kiffer, V., Katz, N., & Lorena, A. C. (2026). Filtering instances and rejecting predictions to obtain reliable models in healthcare. Machine Learning, 115(1), 15. https://doi.org/10.1007/s10994-025-06941-8
Yang, Y., Khorshidi, H. A., & Aickelin, U. (2024). A review on over-sampling techniques in classification of multi-class imbalanced datasets: Insights for medical problems. Frontiers in Digital Health, 6, 1430245. https://doi.org/10.3389/fdgth.2024.1430245
Yavuz, G., Moghanjoughi, M. K., Dumlu, H., & Çakir, H. İ. (2025). A feature selection method combining filter and wrapper approaches for medical dataset classification. Vietnam Journal of Computer Science, 12(4), 375–393. https://doi.org/10.1142/S2196888824500246
Zhang, S., Wang, T., Worden, K., Sun, L., & Cross, E. J. (2025). Canonical-correlation-based fast feature selection for structural health monitoring. Mechanical Systems and Signal Processing, 223(1), 111895. https://doi.org/10.1016/j.ymssp.2024.111895
Zhu, C., Wang, Z., Peng, Y., & Xiao, W. (2025). An improved Red-billed blue magpie feature selection algorithm for medical data processing. PLoS ONE, 20(5), e0324866. https://doi.org/10.1371/journal.pone.0324866
Zhu, J., Zhang, R., & Zhang, H. (2024). An MRI brain tumor segmentation method based on improved U-Net. Mathematical Biosciences and Engineering, 21(1), 778–791. https://doi.org/10.3934/mbe.2024033