Autism Spectrum Disorder Diagnosis Assistance using Machine Learning

Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques be used, such as diagnostic scales contain specific questionnaires formulated by specialists serve guide in the process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; well other characteristics could influence diagnosis of Experiments were carried out list which attributes would truly relevant ASD using ML, great value medical students residents, physicians who not The experiments have shown it possible reduce number only 5 while maintaining an Accuracy above 0.9. Database maintain same level Accuracy, fewer attribute numbers 7. Support Vector stood from others algorithms used obtaining superior all scenarios.