Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis
Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis
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DOI：
10.1038/s41598-020-65384-4
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发表时间：
2020-05-20
影响因子：
4.6
通讯作者：
Casson, Alexander J.
中科院分区：
文献类型：
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作者：
Vabalas, Andrius;Gowen, Emma;Casson, Alexander J.
自闭症是一种发育状况，目前由专家通过观察、访谈和问卷调查技术确定，主要评估社会和沟通缺陷。运动功能和运动模仿在自闭症中也会发生改变，可以更客观地测量。在这项研究中，来自运动模仿任务的运动和眼动追踪数据与监督机器学习方法相结合，对22名自闭症和22名非自闭症成年人进行了分类。重点是一个可靠的机器学习应用程序。我们使用嵌套验证来开发模型，并使用独立的数据样本进一步测试模型。特征选择以选择稳定性为目标，保证结果的可解释性。我们的模型预测运动学特征的诊断准确率为73%，眼动特征的准确率为70%，综合特征的准确率为78%。为了更好地理解自闭症患者的运动模仿差异，我们进一步探索了对预测最重要的特征。与行为结果一致，大多数区别特征来自实验条件，非自闭症个体倾向于成功模仿异常运动运动学，而自闭症个体倾向于失败。机器学习的结果表明，未来的工作可以通过提供定量测试来补充当前的定性测试来帮助诊断过程。
Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism and can be measured more objectively. In this study, motion and eye tracking data from a movement imitation task were combined with supervised machine learning methods to classify 22 autistic and 22 non-autistic adults. The focus was on a reliable machine learning application. We have used nested validation to develop models and further tested the models with an independent data sample. Feature selection was aimed at selection stability to assure result interpretability. Our models predicted diagnosis with 73% accuracy from kinematic features, 70% accuracy from eye movement features and 78% accuracy from combined features. We further explored features which were most important for predictions to better understand movement imitation differences in autism. Consistent with the behavioural results, most discriminative features were from the experimental condition in which non-autistic individuals tended to successfully imitate unusual movement kinematics while autistic individuals tended to fail. Machine learning results show promise that future work could aid in the diagnosis process by providing quantitative tests to supplement current qualitative ones.