Newly Proposed Technique for Autism Spectrum Disorder Based Machine Learning

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.