Multimodal Machine Learning and Interactive Visualization for Early Detection of Autoimmune Flare Dynamics via Multi-Omics Integration

This research predicts flare-ups of autoimmune disease, where the flare-up prediction is performed through the use of multi-omics data. Fast k-Nearest Neighbor’s (k-NN) with SARSA (State-Action-Reward-State-Action) is the machine learning methodology that breaks the model training for the prediction of flares into seven biological features. The first task to be undertaken is concerned with rescaling practitioners' needs in order. Example: all features can be on the same lexical scale. After the Rescaling, the features undergo a ranking as a process of determining the most useful ones for prediction tasks. Then, the features of the fast-kNN training model are used to perform fast training of kNN to allow easy data classification. Lastly, the three steps of SARSA learning, that is, one that shows that data expectation changes and the ordinary expectation of the data can be used to change the prediction model. SARSA learning is used in this situation for the stimulation of SARSA prediction to make the data interactions better for the prediction model explanation. The results suggest that a better combination of Fast-kNN and SARSA can improve the predictions of the target. It may address the need for a better early warning for a flare-up, allowing for better management of the autoimmune disease and treatment.