PREDICTIVE ANALYTICS IN AUTISM SPECTRUM DISORDER: ENHANCING CLINICAL DECISION-MAKING THROUGH MACHINE LEARNING

Autism spectrum disorder (ASD) is a Neuro complaint that influences an individual’s communication, announcement, and knowledge talents. Analysis of Autism can be completed at any age-group level. Autism patients look at diverse kinds of disputes learning disabilities, and complexity with meditation. Mental health problems, motor difficulties, and sensory problems are some of the problems faced by Autism patients. Earlier diagnosis and proper medication at the early stage are essential to control ASD. The ASD prediction framework is built to support a behavioural aspect-based analysis model without any device in this research. The ASD prediction process is focused on the childhood and adolescent analysis model utilized in the system. The behavioural parameters are collected with the support of the Autism Query collections. The decision tree (DT) and Support Vector Machine (SVM) techniques, K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) are applied for the ASD prediction process. The Correlated Feature selection based Random Forest (CFS-RT) algorithm is applied for the ASD prediction process, giving an accuracy of 93.03%, and ANN produces 97.68% and outperformance other methods.