Department of Computer Science, School of Arts and Sciences, University of Central Asia, Naryn, Kyrgyzstan.
Department of Computer Science & IT, University of Lakki Marwat, KPK 28420, Pakistan.
J Healthc Eng. 2023 Jul 10;2023:4853800. doi: 10.1155/2023/4853800. eCollection 2023.
Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disabilities that are chronic or limited in the development of socio-communication skills, thinking abilities, activities, and behavior. In children aged two to three years, the symptoms of autism are more evident and easier to recognize. The major part of the existing literature on autism spectrum disorder is covered by a prediction system based on traditional machine learning algorithms such as support vector machine, random forest, multiple layer perceptron, naive Bayes, convolution neural network, and deep neural network. The proposed models are validated by using performance measurement parameters such as accuracy, precision, and recall. In this research, autism spectrum disorder prediction has been investigated and compared using common parameters such as application type, simulation method, comparison methodology, and input data. The key purpose of this study is to give a centralized framework to use for researchers working on autism spectrum disorder prediction. The best results were obtained by using the random forest algorithm as it performs better than other traditional machine learning algorithms. The achieved accuracy is 89.23%. The workflow representations of the investigated frameworks assist readers in comprehending the fundamental workings and architectures of these frameworks.
自闭症谱系障碍是一种严重的、终身的神经发育疾病，其特征是社会沟通技能、思维能力、活动和行为的发展慢性或有限。在 2 至 3 岁的儿童中，自闭症的症状更为明显，更容易识别。现有的自闭症谱系障碍文献主要涵盖了基于传统机器学习算法的预测系统，如支持向量机、随机森林、多层感知机、朴素贝叶斯、卷积神经网络和深度神经网络。所提出的模型通过使用准确性、精度和召回率等性能测量参数进行验证。在这项研究中，使用常见的参数，如应用类型、模拟方法、比较方法和输入数据，研究并比较了自闭症谱系障碍的预测。本研究的主要目的是为从事自闭症谱系障碍预测的研究人员提供一个集中的框架。随机森林算法的结果最好，因为它比其他传统机器学习算法表现更好。获得的准确率为 89.23%。所调查框架的工作流程表示有助于读者理解这些框架的基本工作原理和架构。
