A novel Harris Hawks Optimization-based clustering method for elucidating genetic associations in osteoarthritis and Diverse Cancer Types

Abstract

Considering the high incidence of osteoarthritis (OA), especially of the knee and hip, this study explores the possible genetic associations between OA and cancer types, including cancers of the bladder, kidney, breast, and prostate. The objective of our study is to decipher the complex genetic connections among these common disorders, emphasizing potential correlations and underlying biological processes. However, the genetic connections between these diseases remain largely unexplored. It fills a vacuum in the literature by using a new clustering approach based on Harris Hawks Optimization (HHO-C), which is a first for applying machine learning methods to this particular set of genetic data. To address this gap, we introduce HHO-C, a novel machine learning-based clustering approach, for the first time in this specific genetic dataset. The work accomplishes three noteworthy firsts: firstly, it is the first to apply machine learning to the study of the genetic interactions between OA and these cancers. Second, it creates a flexible genetic dataset that will be very helpful for further studies in this field. Finally, it presents the novel HHO-C approach, showcasing how well it manages intricate genetic data and providing fresh perspectives on genetic data analysis. It is anticipated that the results of this investigation will clarify the genetic relationships between OA and these malignancies, which could result in novel understandings of medical genetics and the creation of fresh approaches to diagnosis and treatment.