Evidence map›Paper›PMID 41699763›Full record

ArticleBrain and behavior2026

Predicting Autism Spectrum Disorder in Children Using Glowworm Optimization With Extreme Learning Machine Networks.

Vijay Govindarajan, Ashit Kumar Dutta, Zaffar Ahmed Shaikh, Amr Yousef, Mohd Anjum, Sana Shahab

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Vijay GovindarajanDistribution and Supply Technology, Expedia Group, Seattle, Washington, USA.
Ashit Kumar DuttaDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Dariyah, Riyadh, Saudi Arabia.
Zaffar Ahmed ShaikhDepartment of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, Pakistan.
Amr YousefElectrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia.
Mohd AnjumDepartment of Computer Engineering, Aligarh Muslim University, Aligarh, India.
Sana ShahabDepartment of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

AlMaarefa University MHIRSP2025017Princess Nourah Bint Abdulrahman University PNURSP2026R259University of Business and Technology
6 · The paper itself

Abstract

purposeThe earlier prediction of autism spectrum disorder (ASD) placed a serious attention on ensuring the appropriate intervention to improve the child's behavioral, cognitive, and social development. The previous detection process is commonly time-intensive, subjective, and highly dependent on the clinical professions, which leads to limited accessibility in rural areas. The difficulties are addressed by introducing effective ASD detection systems, which provide a scalable, objective, and fast solution, reducing the challenges in the healthcare environment.

methodThis work integrates the Glowworm Optimization with Extreme Learning Machine Networks (GO-ELMN) model to enhance the efficiency of ASD prediction. During the analysis, ASD screening data for children are collected and processed frequently to obtain behavioral, demographic, and medical features. The extracted features are processed by an extraction learning technique, in which the network hyperparameters are optimized using the glowworm optimization algorithm. The optimized classifier recognizes children's behavior by addressing the issues of limited and imbalanced data.

findingsThe efficiency of the system is evaluated using experimental results, in which the system ensures high accuracy and convergence speed.

conclusionThe ASD detection model provides an interpretable, fast, and reliable solution that is effectively utilized in the pediatric healthcare domain.

Indexed as

Autism Spectrum DisorderExtreme Learning MachinesAlgorithmsChildClassification AlgorithmsFemaleHumansMalePrediction AlgorithmsPredictive Learning Modelsautism spectrum disorderglowworm optimization with extreme learning machine networkshyperparameterspediatric healthcare

Identifiers

PMID41699763
PMCPMC12909284

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.