A Deep Learning Based Approach In The Prediction Of Tinnitus Disease For Large Population Data

Tinnitus is a frequent sensory disorder that puts a lot of strain on the patient. Usually, tinnitus results from disturbances occurring to the sensory systems, such as the peripheral seldom central, the somatosensory system, the head and neck, or a mix of the two. This can be found in people with high stress, anxiety, depression, and hearing disorders. Although there is progress in the medical domain using artificial intelligence (AI), research related to tinnitus using AI is limited. This work aims to bridge the gap using deep-learning techniques for evaluating the patient record by examining various parameters. The proposed research also aims to target the same to understand the severity and possible recommendations for tinnitus disease. Our findings forecast how patients will react to tinnitus treatments. From the patients' electroencephalography (EEG) data, predictive EEG variables are extracted, and later feature selection approaches are used to determine the prominent features. The patient's EEG features are supplemented by AI algorithms for training and forecasting treatment outcomes. Higher accuracy levels of the proposed model using AI help the practitioners suggest the proper diagnosis for the patients and also check the patient's recovery over a period of time.