Microarray Gene Expression Dataset Feature Selection and Classification with Swarm Optimization to Diagnosis Diseases

Bioinformatic data concentrated on the accumulation of data pace in the undesired information. Bioinformatics data has vast data-intensive biological information through the computation of data. However, bioinformatics data utilizes statistical methods with gene expression for cancer diagnosis and prognosis. Microarray data provides rough approximations for gene expression analysis. Microarray dataset evaluates the massive gene features presence of sample size and characteristics of microarray data. Hence, it is necessary to evaluate the features in the microarray dataset to exhibit effective outcomes through patterns of gene expression. This paper presented a re-sampling of random probability Swarm Optimization (RRP_SW). With RRP_SW model uses the random re-sampling model estimation of features. The features are evaluated through the computation of a multi-objective optimization model. In the microarray, dataset re-sampling estimated the features in the datasets. The features are samples through the computation of probability values in the datasets for classification. With the RRP_SW model, extreme learning is utilized for the classification of features in the microarray dataset with the benchmark datasets.

@article{Krishna2024,
  title     = {Microarray Gene Expression Dataset Feature Selection and Classification with Swarm Optimization to Diagnosis Diseases},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Peddarapu Rama Krishna and Pothuraju Rajarajeswari},
  doi       = {10.14569/IJACSA.2024.0150753},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150753}
}