Feature Selection and Classification of Microarray Cancer Information System: Review and Challenges

The sickness of cancer, a fatal condition, is caused by the abnormal proliferation of cells in the body. Microarray technology has become famous for diagnosing such serious diseases. Developing a swift and precise method for detecting cancer and discovering drugs that can help eliminate the disease are crucial. The microarray data presents a significant challenge for accurate classification due to its high number of attributes and a relatively small sample size. A noisy, irrelevant, and redundant gene is also present in these microarray genes, resulting in poor diagnosis and categorization. Researchers used machine learning techniques to extract the most significant aspects of gene expression data to achieve this goal. This study explores microarray data, encompassing feature selection, cancer-specific classification algorithms, and future scopes in this field.