Predicting Autism Spectrum Disorder in Children Using Glowworm Optimization With Extreme Learning Machine Networks
Vijay Govindarajan1, Ashit Kumar Dutta2,3, Zaffar Ahmed Shaikh4,5
1Distribution and Supply Technology, Expedia Group, Seattle, Washington, USA.
Insights
This study introduces an optimized model for early autism spectrum disorder (ASD) detection, offering a fast and accurate solution. The system aims to improve accessibility and intervention for children
Area of Science:
- Pediatric Healthcare
- Machine Learning Applications
- Developmental Disorders
Background:
- Early prediction of autism spectrum disorder (ASD) is crucial for timely intervention, improving developmental outcomes.
- Current ASD detection methods are often time-consuming, subjective, and lack accessibility, particularly in rural areas.
- Developing scalable, objective, and rapid ASD detection systems is essential to overcome healthcare challenges.
Purpose of the Study:
- To enhance the efficiency and accuracy of autism spectrum disorder (ASD) prediction.
- To address limitations in current ASD detection processes, including time constraints and accessibility issues.
- To provide a reliable and scalable solution for early ASD identification in pediatric healthcare.
Main Methods:
- Integration of the Glowworm Optimization with Extreme Learning Machine Networks (GO-ELMN) model for ASD prediction.
- Utilizing behavioral, demographic, and medical features from children's ASD screening data.
- Optimizing network hyperparameters with the glowworm optimization algorithm to handle limited and imbalanced data.
Main Results:
- The GO-ELMN model demonstrated high accuracy in ASD prediction.
- The system achieved a fast convergence speed, indicating computational efficiency.
- Experimental results validated the system's effectiveness in identifying children's behavior patterns.
Conclusions:
- The developed ASD detection model offers an interpretable, fast, and reliable solution.
- The system is suitable for effective utilization within the pediatric healthcare domain.
- This approach addresses key challenges in early autism spectrum disorder identification.
Purpose:
The 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.
Method:
This 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.
Findings:
The efficiency of the system is evaluated using experimental results, in which the system ensures high accuracy and convergence speed.
Conclusion:
The ASD detection model provides an interpretable, fast, and reliable solution that is effectively utilized in the pediatric healthcare domain.
