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• W4284976431 abstract "Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual." @default.
• W4284976431 created "2022-07-10" @default.
• W4284976431 creator A5003037161 @default.
• W4284976431 creator A5013837562 @default.
• W4284976431 creator A5038203377 @default.
• W4284976431 date "2022-07-08" @default.
• W4284976431 modified "2023-10-06" @default.
• W4284976431 title "Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children" @default.
• W4284976431 cites W1595900569 @default.
• W4284976431 cites W1978733227 @default.
• W4284976431 cites W2009953021 @default.
• W4284976431 cites W2010549559 @default.
• W4284976431 cites W2012330206 @default.
• W4284976431 cites W2018817659 @default.
• W4284976431 cites W2027299889 @default.
• W4284976431 cites W2032991527 @default.
• W4284976431 cites W2042470723 @default.
• W4284976431 cites W2066144872 @default.
• W4284976431 cites W2084260650 @default.
• W4284976431 cites W2084515485 @default.
• W4284976431 cites W2121056219 @default.
• W4284976431 cites W2126273841 @default.
• W4284976431 cites W2133323742 @default.
• W4284976431 cites W2133416691 @default.
• W4284976431 cites W2138001941 @default.
• W4284976431 cites W2143882983 @default.
• W4284976431 cites W2156107585 @default.
• W4284976431 cites W2163066211 @default.
• W4284976431 cites W2281762137 @default.
• W4284976431 cites W2322983841 @default.
• W4284976431 cites W2523347043 @default.
• W4284976431 cites W2554957217 @default.
• W4284976431 cites W2569531558 @default.
• W4284976431 cites W2580595540 @default.
• W4284976431 cites W2590328111 @default.
• W4284976431 cites W2590910803 @default.
• W4284976431 cites W2607276191 @default.
• W4284976431 cites W2616952103 @default.
• W4284976431 cites W2713311723 @default.
• W4284976431 cites W2740902754 @default.
• W4284976431 cites W2746145198 @default.
• W4284976431 cites W2752558629 @default.
• W4284976431 cites W2762517398 @default.
• W4284976431 cites W2766513404 @default.
• W4284976431 cites W2773922387 @default.
• W4284976431 cites W2790175216 @default.
• W4284976431 cites W2801745895 @default.
• W4284976431 cites W2803679388 @default.
• W4284976431 cites W2806614401 @default.
• W4284976431 cites W2885957996 @default.
• W4284976431 cites W2897843317 @default.
• W4284976431 cites W2898535604 @default.
• W4284976431 cites W2901427516 @default.
• W4284976431 cites W2902092143 @default.
• W4284976431 cites W2911043921 @default.
• W4284976431 cites W2911941403 @default.
• W4284976431 cites W2912080696 @default.
• W4284976431 cites W2912720239 @default.
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• W4284976431 cites W2937831143 @default.
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• W4284976431 cites W2941892929 @default.
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• W4284976431 cites W2955286582 @default.
• W4284976431 cites W2963261317 @default.
• W4284976431 cites W2968778218 @default.
• W4284976431 cites W2985958856 @default.
• W4284976431 cites W2997272625 @default.
• W4284976431 cites W2998205609 @default.
• W4284976431 cites W2999521969 @default.
• W4284976431 cites W3007075053 @default.
• W4284976431 cites W3016163744 @default.
• W4284976431 cites W3023823933 @default.
• W4284976431 cites W3027179734 @default.
• W4284976431 cites W3049615315 @default.
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• W4284976431 cites W3175885997 @default.
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• W4284976431 doi "https://doi.org/10.1155/2022/5766386" @default.
• W4284976431 hasPublicationYear "2022" @default.
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