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      Retracted: Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques

      retraction
      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. Wiley and Hindawi regret that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques

          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.
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            Author and article information

            Contributors
            Journal
            J Healthc Eng
            J Healthc Eng
            JHE
            Journal of Healthcare Engineering
            Hindawi
            2040-2295
            2040-2309
            2023
            29 November 2023
            29 November 2023
            : 2023
            : 9815989
            Affiliations
            Article
            10.1155/2023/9815989
            10699933
            38078175
            7d7f2685-f380-4e7d-9b51-6c7363ab2a3f
            Copyright © 2023 Journal of Healthcare Engineering.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 28 November 2023
            : 28 November 2023
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            Retraction

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