Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis

hybrid-feature-selection-and-classification-model-using-high-dimensional-data-based-on-a-metaheuristic-algorithm-for-brain-cancer-diagnosis
Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis

References

  1. Feltes, B. C., Chandelier, E. B., Grisci, B. I. & Dorn, M. CuMiDa (2019) : An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. J. Comput. Biol. 26(4), 376–386. https://doi.org/10.1089/cmb.2018.0238 (2019).

    Google Scholar 

  2. SREEDEVI, E. & PRADEEP JANGIR, D. S. KUMAR, J. R., An Enhanced Early Detection and Risk Prediction Of Brain Tumors Using MRI-CT Scans With Deep Learning Technique. Journal Theoretical Appl. Inform. Technology, 102(21), 7780–7792 (2024).

  3. Abdul Rasool Hassan, B., Mohammed, A. H., Hallit, S., Malaeb, D. & Hosseini, H. Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: Present achievements and future outlook. Front. Oncol. 15, 1475893. https://doi.org/10.3389/fonc.2025.1475893 (2025).

    Google Scholar 

  4. Salma, R. A. et al. Leveraging machine learning for effective breast cancer diagnosis. WSEAS Trans. Comput. Res. 13, 34–46. https://doi.org/10.37394/232018.2025.13.4 (2025).

    Google Scholar 

  5. Erdal, H. & Namli, E. Monthly streamflow prediction: The power of ensemble machine learning based decision support models. Int. J. Hydrol. Sci. Technol. 1(1), 1. https://doi.org/10.1504/ijhst.2022.10046854 (20233).

    Google Scholar 

  6. Ghorai, S., Mukherjee, A., Sengupta, S. & Dutta, P. K. Multicategory cancer classification from gene expression data by Multiclass NPPC Ensemble. In Proceedings of the International Conference on Systems in Medicine and Biology, 41–48. (2010). https://doi.org/10.1109/icsmb.2010.5735343

  7. Bhandari, N., Walambe, R., Kotecha, K. & Khare, S. P. A comprehensive survey on computational learning methods for analysis of gene expression data. Front. Mol. Biosci. 9, 907150. https://doi.org/10.3389/fmolb.2022.907150 (2022).

    Google Scholar 

  8. Madhu, G., Mohamed, A. W., Kautish, S., Shah, M. A. & Ali, I. Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks. Sci. Rep. 13(1), 13377. https://doi.org/10.1038/s41598-023-40317-z (2023).

    Google Scholar 

  9. Saeys, Y., Inza, I. & Larrañaga, P. A review of feature selection techniques. Bioinf. Bioinformatics 23(19), 2507–2517. https://doi.org/10.1093/bioinformatics/btm344 (2007).

    Google Scholar 

  10. Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 91. https://doi.org/10.1186/1471-2105-7-91 (2006).

    Google Scholar 

  11. Feng, C., Zhang, Z. & Pal, N. R. A comprehensive study on feature selection in the wrapper framework. Int. J. Mach. Learn. Cybern. 11, 1603–1626 (2020).

    Google Scholar 

  12. Zhang, Z., Kuhn, M. & Lalonde, M. Feature selection strategies for high-dimensional data in bioinformatics.. Curr. Opin. Biotechnol. 73, 148–154 (2022).

    Google Scholar 

  13. Chandrashekar, G. & Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024 (2014).

    Google Scholar 

  14. Joseph, J. A. et al. Artificial Intelligence Method for Detecting Brain Cancer Using Advanced Intelligent Algorithms. In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1482–1487). IEEE. (2023)., July https://doi.org/10.1109/ICESC57686.2023.10193659

  15. Heidari, A. A. et al. Harris Hawks optimization: Algorithm and applications. Future Gener. Comput. Syst. 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028 (2019).

    Google Scholar 

  16. Ramadan, O. I. et al. Enhancing breast cancer classification based on BPSO feature selection and machine learning techniques. Eng. Technol. Appl. Sci. Res. 15(3), 23907–23916. https://doi.org/10.48084/etasr.10900 (2025).

    Google Scholar 

  17. Abatal, A. et al. Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency. International Journal of Electrical and Computer Engineering (IJECE) 15(1), 669–676. https://doi.org/10.11591/ijece.v15i1.pp669-676 (2025).

    Google Scholar 

  18. Al Sukhni, H. et al. Brain tumor detection: Integrating machine learning and deep learning for robust brain tumor classification. J. Intell. Syst. Internet Things https://doi.org/10.54216/JISIoT.150101 (2025).

    Google Scholar 

  19. Aljanabi, M., Shkoukani, M. & Hijjawi, M. Ground-level ozone prediction using machine learning techniques: A case study in Amman, Jordan. Int. J. Autom. Comput. 17(5), 667–677. https://doi.org/10.1007/s11633-020-1233-4 (2020).

    Google Scholar 

  20. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines. IEEE Intelligent Systems and their Applications 13(4), 18–28. https://doi.org/10.1109/5254.708428 (1998).

    Google Scholar 

  21. Abdel-Basset, M., Abdel-Fatah, L. & Sangaiah, A. K. Metaheuristic algorithms: A comprehensive review. Comput. Intell. multimedia big data cloud Eng. Appl. 185–231. https://doi.org/10.1016/B978-0-12-813314-9.00010-4 (2018).

  22. Bohat, V. K., Hashim, F. A., Batra, H. & Abd Elaziz, M. Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants. Knowl. Based Syst. 322, 113548. https://doi.org/10.1016/j.knosys.2025.113548 (2025).

    Google Scholar 

  23. Mezura-Montes, E., Velázquez-Reyes, J. & Coello Coello, C. A. A comparative study of differential evolution variants for global optimization. In Proceedings of the 8th annual conference on Genetic and Evolutionary Computation (pp. 485–492). (2006)., July https://doi.org/10.1145/1143997.1144086

  24. Shukla, A. K. Chaos teaching learning-based algorithm for large-scale global optimization problem and its application. Concurrency Comput. Pract. Exp. https://doi.org/10.1002/cpe.6514 (2022).

    Google Scholar 

  25. Tripathi, D., Shukla, A. K. & Reddy, R. Multi-layer hybrid credit scoring model based on feature selection, ensemble learning, and ensemble classifier. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. (2020). https://doi.org/10.4018/978-1-5225-9643-1.ch021

  26. Shukla, A. K. Simultaneously feature selection and parameters optimization by teaching–learning and genetic algorithms for diagnosis of breast cancer. Int. J. Data Sci. Anal. https://doi.org/10.1007/s41060-024-00513-0 (2024).

    Google Scholar 

  27. Shukla, A. K., Singh, P. & Vardhan, M. Hybrid TLBO-GSA strategy for constrained and unconstrained engineering optimization functions. In Hybrid Metaheuristics Research and Applications. (2018). https://doi.org/10.1142/9789813270237_0002

  28. Singh, R. K. & Sivabalakrishnan, M. Feature Selection of Gene Expression Data for Cancer Classification: A Review. Procedia Comput. Sci. 2015, 50, 52–57. (2015). https://doi.org/10.1016/j.procs.2015.04.060

  29. Kilicarslan, S., Adem, K. & Celik, M. Diagnosis and classification of cancer using a hybrid ReliefF and convolutional neural network model. Med. Hypotheses 137, 109577. https://doi.org/10.1016/j.mehy.2020.109577 (2020).

    Google Scholar 

  30. Elemam, T. & Elshrkawey, M. A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis. The Scientific World Journal, 2022. (2022). https://doi.org/10.1155/2022/1056490

  31. Qaraad, M. et al. A hybrid feature selection optimization model for high-dimension data classification. IEEE Access 9, 42884–42895. https://doi.org/10.1109/ACCESS.2021.3065341 (2021).

    Google Scholar 

  32. Ali, W. & Saeed, F. 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 (2023).

    Google Scholar 

  33. Debata, P. P. & Mohapatra, P. A hybrid convolutional neural network approach for feature selection and disease classification. Turk. J. Electr. Eng. Comput. Sci. 29(8), 2580–2599. https://doi.org/10.3906/elk-2105-43 (2021).

    Google Scholar 

  34. Saeid, M. M., Nossair, Z. B. & Saleh, M. A. A microarray cancer classification technique based on discrete wavelet transforms for data reduction and a genetic algorithm for feature selection. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 857–861). IEEE. (2020)., June https://doi.org/10.1109/ICOEI48184.2020.9143024

  35. Passi, K., Nour, A. & Jain, C. K. Markov blanket: Efficient strategy for feature subset selection method for high dimensional microarray cancer datasets. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1864–1871). IEEE. (2017)., November https://doi.org/10.1109/BIBM.2017.8217942

  36. Pashaei, E. & Pashaei, E. Hybrid binary COOT algorithm with simulated annealing for feature selection in high-dimensional microarray data. Neural Comput. Appl. 35(1), 353–374. https://doi.org/10.1007/s00521-022-07780-7 (2023).

    Google Scholar 

  37. Yaqoob, A., Verma, N. K. & Aziz, R. M. Optimizing gene selection and cancer classification with hybrid sine cosine and cuckoo search algorithm. J. Med. Syst. 48(1), 10. https://doi.org/10.1007/s10916-023-02031-1 (2024).

    Google Scholar 

  38. Vatankhah, M. & Momenzadeh, M. Self-regularized Lasso is used to select the most informative features in microarray cancer classification. Multimedia Tools Appl. 83(2), 5955–5970. https://doi.org/10.1007/s11042-023-15207-1 (2024).

    Google Scholar 

  39. Shiny, K. V. Brain tumor segmentation and classification using optimized U-Net. Imaging Sci. J. 72(2), 204–219. https://doi.org/10.1080/13682199.2023.2200614 (2024).

    Google Scholar 

  40. Hira, Z. M. & Gillies, D. F. A review of feature selection and feature extraction methods applied to microarray data. Advances in bioinformatics, 2015. (2015). https://doi.org/10.1155/2015/198363

  41. Das, S. Filters, wrappers, and a boosting-based hybrid for feature selection. In Icml (Vol. 1, pp. 74–81). (2001), June.

  42. Agrawal, P., Abutarboush, H. F., Ganesh, T. & Mohamed, A. W. Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019). Ieee Access 9, 26766–26791. https://doi.org/10.1109/ACCESS.2021.3056407 (2021).

    Google Scholar 

  43. Arasteh, B., Sadegi, R., Aghaei, B. & Ghanbarzadeh, R. Single and multi-objective metaheuristic algorithms and their applications in software maintenance. Decision-Making Models. 97–110. https://doi.org/10.1016/B978-0-443-16147-6.00010-4 (2024).

  44. Tawhid, M. A. & Ibrahim, A. M. Feature selection is based on a rough set approach, wrapper approach, and binary whale optimization algorithm. Int. J. Mach. Learn. Cybern. 11, 573–602. https://doi.org/10.1007/s13042-019-00996-5 (2020).

    Google Scholar 

  45. Xue, B., Zhang, M., Browne, W. N. & Yao, X. A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2015).

    Google Scholar 

  46. El Akadi, A., Amine, A., El Ouardighi, A. & Aboutajdine, D. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper. Knowl. Inf. Syst. 26, 487–500. https://doi.org/10.1007/s10115-010-0288-x (2011).

    Google Scholar 

  47. Yan, X. & Jia, M. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection. Knowl. Based Syst. 163, 450–471. https://doi.org/10.1016/j.knosys.2018.09.004 (2019).

    Google Scholar 

  48. Shen, L. et al. Evolving support vector machines using fruit fly optimization for medical data classification. Knowl. Based Syst. 96, 61–75. https://doi.org/10.1016/j.knosys.2016.01.002 (2016).

    Google Scholar 

  49. Faris, H. et al. An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl. Based Syst. 154, 43–67. https://doi.org/10.1016/j.knosys.2018.05.009 (2018).

    Google Scholar 

  50. Kumar, M., Kulkarni, A. J. & Satapathy, S. C. Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Future Gener. Comput. Syst. 81, 252–272. https://doi.org/10.1016/j.future.2017.10.052 (2018).

    Google Scholar 

  51. Rao, R. V., Savsani, V. J. & Vakharia, D. P. Teaching–learning-based optimization: An optimization method for continuous non-linear large-scale problems. Inf. Sci. 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006 (2012).

    Google Scholar 

  52. Tamimi, E., Ebadi, H. & Kiani, A. Evaluation of different metaheuristic optimization algorithms in feature selection and parameter determination in SVM classification. Arab. J. Geosci. 10, 1–19. https://doi.org/10.1007/s12517-017-3254-z (2017).

    Google Scholar 

  53. Zhou, J. et al. Optimization of support vector machine through metaheuristic algorithms in forecasting TBM advance rate. Eng. Appl. Artif. Intell. 97, 104015. https://doi.org/10.1016/j.engappai.2020.104015 (2021).

    Google Scholar 

  54. Ardjani, F., Sadouni, K. & Benyettou, M. Optimization of SVM multiclass by particle swarm (PSO-SVM). In 2010 2nd International Workshop on Database Technology and Applications (pp. 1–4). IEEE. (2010)., November https://doi.org/10.5815/ijmecs.2010.02.05

  55. Indraswari, R. & Arifin, A. Z. RBF kernel optimization method with particle swarm optimization on SVM using the analysis of input data’s movement. Jurnal Ilmu Komputer dan Informasi 10(1), 36–42. https://doi.org/10.21609/jiki.v10i1.410 (2017).

    Google Scholar 

  56. GSE50161. GEO accession in CuMiDa (Curated Microarray Database), brain cancer gene expression; 130 samples, five classes, 54,676 probes. URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50161

  57. Onyije, F. M. et al. Risk factors for childhood brain tumors: A systematic review and meta-analysis of observational studies from 1976 to 2022. Cancer Epidemiol. 88, 102510. https://doi.org/10.1016/j.canep.2023.102510 (2024).

    Google Scholar 

  58. Das, P. & Das, A. Multi-scale cross spectral coherence and phase spectral distribution based measurement in non-subsampled shearlet domain for classification of brain tumors. Expert Syst. Appl. 247, 123329. https://doi.org/10.1016/j.eswa.2024.123329 (2024).

    Google Scholar 

  59. Hosack, D. A., Dennis, G. Jr., Sherman, B. T., Lane, H. C. & Lempicki, R. A. Identifying biological themes within lists of genes with EASE. Genome Biol. 4(10), R70. https://doi.org/10.1186/gb-2003-4-10-r70 (2003).

    Google Scholar 

  60. Dennis, G. Jr. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4(9), R60. https://doi.org/10.1186/gb-2003-4-9-r60 (2003).

    Google Scholar 

  61. Uzma, Al-Obeidat, F., Tubaishat, A., Shah, B. & Halim, Z. Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data. Neural Comput. Appl. 34 (11), 8309–8331. https://doi.org/10.1007/s00521-020-05101-4 (2022).

    Google Scholar 

  62. El-Kafrawy, P., Manhrawy, I. I., Fathi, H., Qaraad, M. & Kelany, A. K. Using multi-feature selection with machine learning for de novo acute myeloid leukemia in Egypt. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) (pp. 1–8). IEEE. (2019)., December https://doi.org/10.1109/ISACS48493.2019.9068905

  63. Halim, Z. An ensemble filter-based heuristic approach for cancerous gene expression classification. Knowl.-Based Syst. 234, 107560. https://doi.org/10.1016/j.knosys.2021.107560 (2021).

    Google Scholar 

  64. Abualigah, L., Al-Okbi, N. K., Mirjalili, S., Alshinwan, M., Al Hamad, H., Khasawneh,A. M., … Gandomi, A. H. (2022). Moth-Flame optimization Algorithm, arithmetic optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and sine cosine algorithm: a comparative analysis using multilevel thresholding image segmentation problems. In Handbook of Moth-Flame Optimization Algorithm (pp. 241–263). CRC Press. https://doi.org/10.1201/9781003205326.

  65. Shannaq, F. et al. Exploring metaheuristic optimization algorithms in the context of textual cyberharassment: A systematic review. Expert Syst. 42(2), e13826. https://doi.org/10.1111/exsy.13826 (2025).

    Google Scholar 

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