Fuzzy Logic Feature Selection Method for Earlier Prediction of Autism

Abstract

Autism Spectrum Disorder (ASD) is a serious developmental ailment of an individual’s behaviours, Verbal exchange, learning competencies, and social interplay. The ASD varies among individuals and impacts the neurobiological ailment of development and affects the general cognitive, emotional, social and physical health of the affected individual. The goal of the study is to identify ASD in the early stage to enhance the development of brain. It also educates the parents and the caregivers about ASD. The vital goal of the paper is to deal with new Fuzzy Logic Feature Selection method for the early prediction of Autism. The Proposed method shows the importance of Greedy Search / Correlation Based Feature Selection using Random Forest Machine Learning. The Random Forest Classifier provides higher consequences compared to different Classifiers. The Fuzzy Logic rule has the capability for the decision making system in Autism early prediction in easy, precise and perfect way.