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Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques

Computer Systems Science & Engineering 2022年第4期41卷 389-404页

作者： A.Arunkumar D.Surendran Computer science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India

Difﬁculty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)*** diseases can affect the nerves at any stage of the human being in childhood,adolescence,and *** is known as a behavioral disease due to the appearances of symptoms over theﬁrst two years that continue until *** of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with *** detection of ASD is a very challenging task among various *** learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality *** this paper,ensemble ML techniques for the early detec-tion of ASD are *** 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 *** prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict theﬁnal *** accuracy,sensitivity,and speciﬁcity of the proposed system are calculated using confusion *** 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

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