Autism Spectrum Prognosis using Worm Optimized Extreme Learning Machine (WOEM) Technique

Nowadays, exponentially increasing psychological disorder is Autism Spectrum Disorder (ASD). ASD is caused due to the improper functioning of the brain and even in the change of gene hereditary. This kind of disorder affects the interaction, communication, and learning capabilities of persons. Moreover, it was observed that ASD has a significant impact on the children than the adults. At present, this sort of autism spectrum syndrome is detected a great deal later than is conceivable. Hence early detection of Autism Spectrum Disorders is needed, which grows the overall mental health of persons. In this article, we present the new idea Worm Optimized Extreme Learning Machines (ELM) for early diagnosis of Autism Disorders. The proposed algorithm is intelligent learning algorithm that completely works on the hybrid integration of glowworm optimization and Single feed- forward extreme learning machines. The WOEM algorithm provides stable and accurate decisions in predicting autism disorders. Also, these algorithms are tested with Kaggle ASD datasets and compared with the other machine learning algorithms in which the proposed algorithm outperforms in terms of specificity, Sensitivity as well as accuracy.