DeepNap: An Efficacious Fuzzy Expert Model to Previse Type 1 Narcolepsy and Type 2 Narcolepsy

Abstract

The prediction of neurologically affecting disorders like Narcolepsy in growing adults are highly required to prevent serious brain disorders. The growing trends in the fuzzy expert system is taken as an advantage. This study proposes an efficacious model constructed based on Mamdani Fuzzy Logic prediction in MATLAB software, to predict Type 1 Narcolepsy, Type 2 Narcolepsy, and Other Psychological Disorder with five important patient's clinical data consisting of Excessive Daytime Sleepiness, Cataplexy, Sleep Paralysis, Hallucination, and Insomnia in the early stages of disease development. It predicts both Type 1 Narcolepsy and Type 2 Narcolepsy with an accuracy of about 96.6% separately. This model helps to predict Narcolepsy at earlier stages, making the patient take up earlier medications accordingly.