Abstract
ADHD is a prevalent disorder among the younger population. Standard evaluation techniques currently use evaluation forms, interviews with the patient, and more. However, its symptoms are similar to those of many other disorders like depression, conduct disorder, and oppositional defiant disorder, and these current diagnosis techniques are not very effective. Thus, a sophisticated computing model holds the potential to provide a promising diagnosis solution to this problem. This work attempts to explore methods to diagnose ADHD using combinations of multiple established machine learning techniques like neural networks and SVM models on the ADHD200 dataset and explore the field of neuroscience. In this work, multiclass classification is performed on phenotypic data using an SVM model. The better results have been analyzed on the phenotypic data compared to other supervised learning techniques like Logistic regression, KNN, AdaBoost, etc. In addition, neural networks have been implemented on functional connectivity from the MRI data of a sample of 40 subjects provided to achieve high accuracy without prior knowledge of neuroscience. It is combined with the phenotypic classifier using the ensemble technique to get a binary classifier. It is further trained and tested on 400 out of 824 subjects from the ADHD200 data set and achieved an accuracy of 92.5% for binary classification The training and testing accuracy has been achieved upto 99% using ensemble classifier.
Abstract (translated)
Attention 短缺症(ADHD)是年轻人中常见的一种症状。目前的标准评估技术通常使用评估表、与患者的对话以及更多的方法。然而,ADHD的症状与许多其他障碍的症状类似,例如抑郁症、行为障碍和逆反性情感障碍,而这些目前的诊断技术并不十分有效。因此,一种复杂的计算模型有潜力为这个问题提供一种有前途的诊断解决方案。这项工作试图使用多个已确立的机器学习技术,如神经网络和SVM模型,在ADHD200数据集上组合使用,并探索神经科学领域。在这项工作中,使用SVM模型对症状数据进行多分类。与Logistic Regression、KNN、adaBoost等其他监督学习技术相比,症状数据的性能表现更好。此外,从提供的四个样本的MRI数据中构建神经网络,以获得高准确性,而无需神经科学领域的事先知识。它使用集成技术将症状分类器与SVM模型组合起来,得到二进制分类器。在ADHD200数据集上对400个样本中的400个进行了训练和测试,实现了二进制分类的准确率为92.5%。使用集成分类器的训练和测试准确率可以达到99%。
URL
https://arxiv.org/abs/2302.00332
