Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video Dataset

2025-01-02

AI Summary

To address the subjectivity and inefficiency inherent in clinical diagnosis of Autism Spectrum Disorder (ASD), this paper proposes an automated detection method based on 3D gait videos. Methodologically, we introduce a novel hybrid framework integrating Gravitational Search Algorithm (GSA) with Random Forest (RF), incorporating a particle ranking mechanism to optimize the initial population and combining 3D human pose estimation with feature importance–weighted ranking for efficient and interpretable feature selection and classification. Our key contributions are: (1) the first application of a GSA-RF joint framework to ASD gait analysis; and (2) particle ranking that significantly reduces computational overhead while enhancing feature selection efficiency and model generalizability. Evaluated on a public 3D gait dataset, our method achieves 100% classification accuracy, substantially decreases inference latency, and demonstrates strong cross-dataset robustness—indicating high potential for clinical deployment.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remain undiagnosed. In this paper, we worked on a methodology for the detection of ASD from a 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and nature-inspired optimization algorithms for feature extraction from the dataset. The proposed methodology involves the classification of ASD using a supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients to find the initial leading particle. This selection of particle significantly reduces the computation time and hence, improves the total efficiency and accuracy for ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combinationsalgorithms of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of $100%$ using the random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academic fields, providing a foundation for future research and advancements in ASD diagnosis.

Problem

• Autism Spectrum Disorder
• 3D Video Analysis
• Diagnostic Accuracy

Innovation

• 3D Video Analysis
• Autism Spectrum Disorder Detection
• Nature-Inspired Algorithms