Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning.

Autor: Munagala NVLMK; Department of Electrical Electronics and Communication Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, Andhra Pradesh 530045, India., Saravanan V; Dambi Dollo University, Dambi Dollo, Ethiopia., Almukhtar FH; Department of Computer Technical Engineering, Imam Ja'afar Al-Sadiq University, Kirkuk, Iraq., Jhamat N; Department of Information Technology, University of the Punjab, Gujranwala Campus, Gujranwala, Pakistan., Kafi N; Department of Computer Science, National University of Computer and Emerging Sciences, Karachi, Pakistan., Khan S; Department of Maths, Stats & Computer Science, The University of Agriculture Peshawar, Peshawar, KP, Pakistan.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Aug 27; Vol. 2022, pp. 4464603. Date of Electronic Publication: 2022 Aug 27 (Print Publication: 2022).
DOI: 10.1155/2022/4464603
Abstrakt: Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 N. V. L. M Krishna Munagala et al.)
Databáze: MEDLINE
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