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

Author:

Munagala N. V. L. M Krishna1ORCID,Saravanan V.2ORCID,Almukhtar Firas Husham3ORCID,Jhamat Naveed4ORCID,Kafi Nadeem5ORCID,Khan Samiullah6ORCID

Affiliation:

1. Department of Electrical Electronics and Communication Engineering, GITAM Institute of Technology, GITAM Deemed University, Visakhapatnam, Andhra Pradesh 530045, India

2. Dambi Dollo University, Dambi Dollo, Ethiopia

3. Department of Computer Technical Engineering, Imam Ja’afar Al-Sadiq University, Kirkuk, Iraq

4. Department of Information Technology, University of the Punjab, Gujranwala Campus, Gujranwala, Pakistan

5. Department of Computer Science, National University of Computer and Emerging Sciences, Karachi, Pakistan

6. Department of Maths, Stats & Computer Science, The University of Agriculture Peshawar, Peshawar, KP, Pakistan

Abstract

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.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cost-sensitive learning for imbalanced medical data: a review;Artificial Intelligence Review;2024-03-01

2. Design and Behavioral Analysis of Students during Examinations using Distributed Machine Learning;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

3. Automated detection of cardiac arrest in human beings using auto encoders;Measurement: Sensors;2023-06

4. Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework;Computational Intelligence and Neuroscience;2023-01-04

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