Prediction of Autoimmune Disease using Ensemble Machine Learning Approaches

The term "autoimmune diseases" refers to a class of illnesses where the immune system attacks the body's own healthy cells. Effective treatment and management of many disorders depend on early detection and precise diagnosis. In this research, Using the clinical and laboratory data were it had been imported from the UCI data repository to develop an ensemble model for the prediction of autoimmune disorders. To create a reliable predictive model, the ensemble model incorporates many machine learning approaches like the Gradient boost classifier, SVC and RF, and the KNN. The suggested model was trained and assessed using a sizable dataset of individuals with autoimmune disorders. When the model's performance was compared to that of individual machine learning methods, it was exposed that the ensemble model gives out them all in terms of accuracy, and sensitivity, and set out with the help of hyper-parameter Tuning.