基于栈式稀疏自编码器的孤独症严重程度预测

Prediction of Autism-Severity Based on Stacked Sparse Autoencoder

有效的早期诊断对孤独症有着重大意义。为此,提出一种综合考虑遗传因素及环境因素预测孤独症严重程度的方法。根据儿童孤独症评定量表(CARS),从孤独症门诊收集了样本集,建立基于栈式稀疏自编码器结合Softmax分类器的预测模型,并与常用的决策树、支持向量机方法进行了比较。经过试验证明,所提出的基于栈式稀疏自编码器的模型预测孤独症严重程度的准确率最高。

Effective early diagnosis was of great significance for autism.An approach was proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism.According to the Childhood Autism Rating Scale(CARS),a sample set was collected from the autism clinic.Then,a predictive model based on stacked sparse autoencoder combined with softmax classifier was constructed and compared with decision trees and support vector machines.Experiments show that the proposed model has a highest accuracy in predicting the severity of autism.