Determination and Diagnosis of Autism Spectrum Disorder using Efficient Machine Learning Algorithm

Autism Spectrum Disorder (ASD) is gaining momentum faster than ever before. Screening tests for autistic features are very expensive and time-consuming. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism relatively early. Autism, commonly known as "Autism Spectrum Disorder" (ASD), is a difficult, chronic developmental impairment that includes issues like lack of focus, repetitive behavior, and nonverbal communication. ASD has been growing more rapidly in recent years, necessitating an early diagnosis. Autism detection necessitates some time-consuming and costly screening tools. Predictive analytics, which is another name for a variety of mathematical models, has become increasingly popular in recent years. Machine learning and pattern recognition in medical research Machine learning and pattern recognition are two multidisciplinary study fields in medicine that offer efficient methods to detect ASD. So, we proposed a system to predict autism spectrum disorder based on various factors given by the user on the front end, using machine learning techniques and algorithms such as KNN, Logistic regression, decision trees random forest, ,the Naive Bayes and XGB classifier.