Early diagnosis of obsessives-compulsive disorder through gene expression analysis using machine learning models

The primary aim of this research study is to develop an early diagnosis method for obsessive-compulsive disorder (OCD) by utilizing gene expression analysis and Machine Learning techniques. Gene expression data from both blood and brain samples were collected from the gene expression omnibus (GEO) database. As OCD cannot currently be detected through instruments, it relies on clinical symptoms that are often misinterpreted. To address this, a novel hybrid feature selection approach that combines statistical and ML methods is proposed to identify down-regulated genes that may play a crucial role in the development of OCD. The results of the earlier studies point to important implications and emphasize the significance of down-regulated gene expression in OCD. Currently, gene expression profiling is used as an investigative tool to identify the specific cell receptors associated with certain conditions, followed by targeted medication to alleviate symptoms. Our proposed method achieved high accuracy rates of 83% for blood data and 92% for brain data when compared to other feature selection methods such as MIFS, CFS, and mRMR, using various Machine Learning models. These results demonstrate the effectiveness of our approach in early OCD diagnosis using gene expression analysis.