Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

• Published in: Journal of medical systems Vol. 48; no. 1; p. 10
• Main Authors: Yaqoob, Abrar, Verma, Navneet Kumar, Aziz, Rabia Musheer
• Format: Journal Article
• Language: English
• Published: New York Springer US 09.01.2024; Springer Nature B.V
• Subjects: Algorithms; Biological activity; Breast cancer; Breast Neoplasms - genetics; Cancer; Classification; Classifiers; Datasets; Feature selection; Female; Gene expression; Genes; Health Informatics; Health Personnel; Health Sciences; Humans; Machine Learning; Medicine; Medicine & Public Health; Optimization; Original Paper; Redundancy; Search algorithms; Statistics for Life Sciences; Support Vector Machine; Support vector machines; Trigonometric functions; Minimum Redundancy Maximum Relevance (mRMR); Sine Cosine Algorithm (SCA); Cuckoo Search Algorithm (CSA); Cancer Classification; Support Vector Machine (SVM); Feature Selection (FS)
• ISSN: 1573-689X; 0148-5598; 1573-689X
• DOI: 10.1007/s10916-023-02031-1

Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data. Graphical Abstract