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.

翻译：自闭症诊断过程漫长且困难，延误可能带来严重后果。人工智能可彻底改变自闭症诊断方式，尤其在难以察觉疾病早期症状的情况下尤为重要。基于AI的诊断工具通过分析海量数据、揭示人类评估者难以直观发现的模式，有助于确认诊断或提示需进一步检查。在实现及时有效的诊断后，可通过人工智能运用多种方法对自闭症进行治疗。本文采用四个数据集，通过联邦学习方法进行数据整合，并利用支持向量分类器方法实现该障碍的早期诊断。该方法在预测自闭症谱系障碍方面达到了99%的准确率，较此前结果提升了13%。