Accurate Autism Spectrum Disorder prediction using Support Vector
  Classifier based on Federated Learning (SVCFL)
Ali Mohammadifar Hasan Samadbin Arman Daliri
The path to an autism diagnosis can be long and difficult, and delays can
have serious consequences. Artificial intelligence can completely change the
way autism is diagnosed, especially when it comes to situations where it is
difficult to see the first signs of the disease. AI-based diagnostic tools may
help confirm a diagnosis or highlight the need for further testing by analyzing
large volumes of data and uncovering patterns that may not be immediately
apparent to human evaluators. After a successful and timely diagnosis, autism
can be treated through artificial intelligence using various methods. In this
article, by using four datasets and gathering them with the federated learning
method and diagnosing them with the support vector classifier method, the early
diagnosis of this disorder has been discussed. In this method, we have achieved
99% accuracy for predicting autism spectrum disorder and we have achieved 13%
improvement in the results.