A Survey on Genetic Disease − Autism Spectrum Disorder Prediction and Classification in Machine Learning

Autism spectrum disorder (ASD) is a hereditary, neurological condition with many aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor communication, and a lack of recurrent interest, may result from ASD. It can be highly advantageous for children to improve their psychological wellness level if the ASD is recognized in the earlier years of life. Furthermore, machine learning (ML) approaches are now essential for diagnosing and categorising ASD. The creation of computer programmes that can acquire data and utilise it to gain knowledge for oneself is the main goal of this aspect of artificial intelligence. Many scholars have suggested various ML strategies for quickly and accurately detecting the various forms of ASD. This paper presents a survey on ASD prediction and classification using ML methods-based research articles from the year 2016 to 2023. Moreover, the current survey article discusses the performance assessment employing different metrics and made a comparative assessment to determine the ML model’s effectiveness. From this survey, it is identified that Artificial Neural Network model has attained better results than other ML algorithms. Moreover, further ASD studies employing an ML strategy for feature selection, prediction and classification can greatly benefit from this research.