Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques  

作　　者：A.Arunkumar D.Surendran 

机构地区：[1]Computer science and Engineering,Sri Krishna College of Engineering and Technology,Coimbatore,641008,India [2]Computer Science and Engineering,KPR Institute of Engineering and Technology,Coimbatore,641407,India

出　　处：《Computer Systems Science & Engineering》2022年第4期389-404,共16页计算机系统科学与工程（英文）

摘　　要：Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human being in childhood,adolescence,and adulthood.ASD is known as a behavioral disease due to the appearances of symptoms over thefirst two years that continue until adulthood.Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD.The detection of ASD is a very challenging task among various researchers.Machine learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality results.In this paper,ensemble ML techniques for the early detec-tion of ASD are proposed.In this detection,the dataset isfirst processed using three ML algorithms such as sequential minimal optimization with support vector machine,Kohonen self-organizing neural network,and random forest algorithm.The prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict thefinal result.The accuracy,sensitivity,and specificity of the proposed system are calculated using confusion matrix.The pro-posed ensemble technique performs better than state-of-the art ML algorithms.

关 键 词：SVM autism disorder Kohonen SONN max voting ensemble machine learning technique random forest SMO–SVM bootstrap gradient boosting 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]