Early Detection Support Mechanism in ASD using ML Classifier - et. al., M.Parvathi, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
In general the medical examinations will be thoroughly done by the expert while considering the ASD symptoms and will be considered as standard while determining the level of existence of ASD. Based on the past literatures on diagnostic criteria written for statistical measures of ASD, over few cores symptoms are identified that causes shortfalls in body language, verbal and communication further leads to societal issues. Few common and most noticeable ASD symptoms include inadequacy in reproducing regular pattern of behavior due to withering of various sensory organs. India has witnessed a paradigm shift in dealing with persons with disabilities over last two decades. Even though, there is raise in cognizance on symptoms and characteristics of ASD among the parents and professions yet the major gap is still continuing in identifying ASD at the early stage and for providing correct treatment accordingly. The existing early detection screening tools are time consuming, tedious, exclusive, and cumbersome. Also results in incomplete diagnostic process due to lack of complete evidence on the child behavior. To address these problems, an attempt is made in this work using K-Means clustering classifier, one of the ML algorithms in addition with support vector machine. ASD features that are usually found in child below three years are taken as inputs to the model and corresponding responses were identified with accuracy of 96% and 80% respectively in the diagnostic process while categorizing the manifestation of cause and effect on each individual capability. The supervised classification SVM is further converted to the unsupervised clustering problem and observed the accuracy level of 95%. The proposed method is mere perfect with good accuracy level while detecting the autism in children in real time environment.