European Journal of Molecular & Clinical Medicine

Autism spectrum disorder (ASD) is a psychiatric disorder which leads to
neurological anddevelopmental growth of a person which starts in early age and gets
carried throughout their life.It is a condition associated with significant healthcare costs
and early diagnosis can reduce these.Unfortunately, waiting time is lengthy for an ASD
diagnosis and it is cost effective. Due to theincrease in economy for autism prediction and
the increase in the number of ASD cases across theworld is in need of easily implemented
and effective screening methods by GUI results. Toovercome the time complexity for
identifying the disorder advanced technologies can be used suchas machine learning
algorithms to improve precision, accuracy and quality of the diagnosisprocess. Machine
learning helps us by providing intelligent techniques to discover the affectedpatient, which
can be utilized in prediction and to improve decision making. And hence, wepropose the
data set features related to autism screening of adult and child to be used for
furtheranalysis and to improve the classification of ASD cases.

Identification of disease at a beginning stage is very essential for higher treatment. It’s a awfully complicated task for medical researchers to predict the illness within the early stages because of delicate symptoms. Typically the symptoms turn out to be evident once it's too late. to beat this issue, this project aims to boost disease designation victimization machine learning approaches. The most objective of this analysis is to use categorization techniques to spot the liver patients from healthy people. This project conjointly aims to match the categorization techniques supported their presentation factors. To serve the medical community for the designation of disease between patients, a graphical computer interface is urbanized victimization python (Node RED). The GUI will be promptly used by doctors and medical practitioners as a screening tool for the disease.