Particle Swarm Optimization based Explainable Deep Learning Models for Celiac Disease Prediction

Celiac disease is an autoimmune chronic disease, which occurs due to the ingestion of gluten, and affects about 1% of the world's population, diagnosed early in life to avoid potential damage afterward. Article on Particle Swarm Optimization-based explainable deep learning model for the prediction of celiac disease is discussed in this section. It applies PSO for feature selection and, therefore, does not offer redundant data to the extent of up to 35% with hyperparameter tuning that improves the efficiency of the model by up to 15%. The attention mechanisms along with SHAP (SHapley Additive exPlanations) would be incorporated in offering insights into the interpretable key genetic and serological factors making the predictions in the optimized deep learning model. It is experimented on some real datasets also, so as to get the validation on a large scale. Then, it also got 94.3% accuracy and resulted in 20% reduction of computation overhead. This approach makes clinical decision with good strong diagnostics.