Leveraging Machine Learning for Biomarker Discovery and Risk Prediction in Autoimmune Disease Genetics

Autoimmune diseases are diseases where the body immune system targets on the body tissues. These conditions are polygenic in that they implicate multiple genes and gene by context interactions. Evaluating the human genomes is a complicated process because it is high dimensionality and epistatic interactions between many genes. Standard biochemical-genetic approaches can fail to provide the answers to these questions, so using supervised and unsupervised ML algorithms has been shown to help in understanding such diseases. Thus, using techniques of ML for processing big genomic data can reveal significant patterns. This work employs the XGBoost classifier to demonstrate examples of applying ML in the field of autoimmune diseases diagnostics based on genetic data. Therefore, we used the feature selection technique, data imputation, clinical and genetic feature engineering, and hyperparameters optimization. The optimizer reaches 94.75% accuracy concluding that others are compatible with inherent genes influencing autoimmune disease risk, such as WBC count. On the basis of feature importance analysis and confusion matrices, I got an idea about the distributive pattern, reasons, and pathways of these diseases. As such, these studies show the promise of ML in the biomarker discovery for screening and in the unraveling of the genetics of autoimmune diseases. As such, the approach provides future directions for the development of psychological therapies individually targeted with genetic vulnerabilities in autoimmune disease management.