Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain before birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior in addition to specific behavioral traits. Hence, this would possibly deteriorate their social behavior among other individuals, as well as their overall interaction within their community. Moreover, medical research has proved that ASD also affects the facial characteristics of its patients, making the syndrome recognizable from distinctive signs within an individual's face. Given that as a motivation behind our work, we propose a novel privacy-preserving federated learning scheme to predict ASD in a certain individual based on their behavioral and facial features, embedding a merging process of both data features through facial feature extraction while respecting patient data privacy. After training behavioral and facial image data on federated machine learning models, promising results are achieved, with 70\% accuracy for the prediction of ASD according to behavioral traits in a federated learning environment, and a 62\% accuracy is reached for the prediction of ASD given an image of the patient's face. Then, we test the behavior of regular as well as federated ML on our merged data, behavioral and facial, where a 65\% accuracy is achieved with the regular logistic regression model and 63\% accuracy with the federated learning model.
翻译:由于胎儿大脑在出生前的改变,ASSD是一种神经发育综合症(ASD),是胚胎大脑在出生前的改变导致的神经发育综合症。除了特定的行为特征外,这种疾病通过特殊的社会限制和重复行为将其患者区分为特殊的社会限制和重复行为,因此,这可能恶化其他个人的社会行为,以及他们在社区内的整体互动。此外,医学研究证明,ASD还影响病人的面部特征,使该综合症可以从个人脸上的明显特征中识别出来。作为我们工作的动力,我们提议了一个新的隐私保护联合学习计划,根据病人的行为和面部特征预测ASD,通过面部特征提取并嵌入两个数据特征的合并过程,同时尊重病人的数据隐私。在对Federated的机器学习模型进行行为和面部图像数据培训后,取得了可喜的结果,根据个人面部学习环境中的行为特征对ASD进行预测的准确度为70 ⁇ 。基于病人面部和面部脸部的图像,我们用正常的准确度测试了第63号面部的准确度。然后,我们用正常的和精确度测试了第65号的精确度,并进行了数据学习。