A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases.

The growing incidence of Non-Hodgkin lymphomas (NHL) in recent times has brought attention to the need for thorough investigations of their genetic associations with autoimmune and rheumatologic conditions, such as systemic lupus, celiac disease, and Sjögren's syndrome. Our study is the first of its type in this field since it uses machine learning to investigate these relationships in great detail. Firstly, we have developed a new genetic dataset, specifically designed to uncover the genetic intricacies of NHL and rheumatologic diseases, offering unprecedented insights into their molecular mechanisms. Following this, we introduced the Clustered-Based Binary Grey Wolf Optimizer (CB-BGWO), a novel method that significantly revolutionizes the feature selection process in genetic analysis. This optimizer significantly improves the accuracy and efficiency of identifying important genetic variables affecting the interaction between rheumatologic and NHL illnesses. This methodological advance not only increases the analytical power but also creates a new standard for genetic research methods. Our findings address a significant gap in the literature and offer valuable insights that could positively support future treatment strategies and research paths. By illuminating the complex genetic connections between NHL and significant rheumatologic conditions, this work contributes to a better understanding and treatment of these complex diseases.