Otistik Spectrum Bozukluğunun Yapay Sinir Ağları ile Tespiti

Abstract

Autistic Spectrum Disorder (ASD) is a neuro-developmental disorder that is congenital or manifests with a delay in social relations and physiological development at an early age, and also causes problems in communication. It is possible to reduce the effect of the disease on individuals with early diagnosis. However, detecting ASD at an early age requires time and cost. In the studies conducted in recent years, it is seen that there is a serious increase in ASD cases. In order to prevent this increase, decision support systems should be established for early diagnosis. It is important to develop decision support models to diagnose ASD, especially for children aged 12-36 months. In this study, a model was developed that can help in detecting ASD with high accuracy for 12-36 months old children. The data set used in the created model was collected from the mobile application named ASDTests developed by Thabtah. In the estimation phase, four different machine learning algorithms which are support vector machine, Naive Bayes,Random Forest and Artificial Neural Network were used. In the classification process, high success rate was obtained with artificial neural network, random forest classifier.