Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)
This work addresses the problem of timely autism diagnosis for patients and clinicians, but it is incremental as it applies existing methods to new data.
The researchers tackled early diagnosis of Autism Spectrum Disorder by combining federated learning with a support vector classifier on four datasets, achieving 99% accuracy and a 13% improvement in results.
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.