Souza Paulo Vitor de Campos, Guimaraes Augusto Junio, Araujo Vanessa Souza, Lughofer Edwin
Department of Knowledge Based Mathematical Systems, Johannes Kepler University, Linz, Austria.
Faculty Una of Betim, Betim, Brazil.
Soft comput. 2021;25(14):9163-9183. doi: 10.1007/s00500-021-05877-0. Epub 2021 May 24.
This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.
本文提出了一种基于神经网络和模糊系统的贝叶斯混合方法，用于构建模糊规则，以协助专家检测与人类自闭症存在相关的特征和关系。本文提出的模型使用通过移动设备生成的数据库，该数据库处理在移动应用程序中回答一系列问题的人类自闭症特征诊断。贝叶斯模型通过在模型的第一层构建高斯模糊神经元和在第二层构建逻辑神经元来工作，以形成一个与人工神经网络相连的模糊推理系统，该人工神经网络激活一个强大的输出神经元。基于包含儿童、成人和青少年自闭症发生率的真实世界数据集，将新的模糊神经网络模型与基于高维的传统先进机器学习模型进行了比较。结果（儿童97.73%/青少年94.32%/成人97.28%）证明了我们的新方法在确定具有自闭症特征的儿童、青少年和成人方面的效率（在所有测试的机器学习模型中名列前茅），可以通过模糊规则生成关于数据集的知识。
