IRO Journals
Journal of Ubiquitous Computing and Communication Technologies

AI-Driven Autism Spectrum Disorder Screening Using LightGBM and Behavioral Analysis

Open Access
Published: 17 October, 2026
Pages: 369-381
  • Arul Princy A. , Arul Princy A.
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
  • Rohini T. , Rohini T.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
  • Visali Visali
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Dindigul, India
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How to Cite

A., Arul Princy, Rohini T., and Visali. 2026. “AI-Driven Autism Spectrum Disorder Screening Using LightGBM and Behavioral Analysis”. Journal of Ubiquitous Computing and Communication Technologies 8 (4): 369-81. https://doi.org/10.36548/jucct.2026.4.005.
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Keywords

Autism Spectrum Disorder
Light GBM
Questionnaire-Based Dataset
Behavioral Analysis
Explainable Artificial Intelligence
SHAP Classification

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by problems with communication, social interaction, and behavior patterns. Early detection of autism spectrum disorder is crucial for appropriate treatment and positive development. The existing procedures are lengthy and rely on manual assessment. This research work proposes a machine learning solution for the early detection of autism spectrum disorder based on the behavioral data obtained from a questionnaire, along with the demographic features. Different approaches to classification, including Decision Tree, K-Nearest Neighbors (KNN), and Light Gradient Boosting Machine (LightGBM) are tested to select the most efficient method for prediction. LightGBM proves to be the best approach because of its efficiency with structured data. In order to improve model transparency and facilitate decision-making in clinical practice, SHapley Additive exPlanations (SHAP) are incorporated to assess and measure the impact of specific features. The developed model ensures 97% accuracy, 97% precision, 92% recall, and 94% F1-score on the test dataset. The feature importance analysis reveals that the behavioral factors such as communication issues, problems in making eye contact, interaction problems, and repetition are among the important features contributing to the prediction of ASD. By combining high predictive accuracy with transparency, the proposed solution presents a reliable, efficient, and easy-to-use approach for early ASD screening.

References

  1. Saad, Khaled, Soha A. Hussain, Ahmad Roshdy Ahmad, Mohamed Shawky Elfarargy, Amira Elhoufey, Abdulrahman A. Al-Atram, Abdelrahman N. Abdelal, Kawashty R. Mohamed, and Mostafa M. Embaby. "The evolving role of machine learning in autism spectrum disorder: current evidence and future directions." Pediatric Research 98, no. 7 (2025): 2438-2440.
  2. Ahmed, Masroor, Sadam Hussain, Farman Ali, Anna Karen Gárate-Escamilla, Ivan Amaya, Gilberto Ochoa-Ruiz, and José Carlos Ortiz-Bayliss. "Summarizing recent developments on autism spectrum disorder detection and classification through machine learning and deep learning techniques." Applied Sciences 15, no. 14 (2025): 8056.
  3. Rajagopalan, Shyam Sundar, Yali Zhang, Ashraf Yahia, and Kristiina Tammimies. "Machine learning prediction of autism spectrum disorder from a minimal set of medical and background information." JAMA Network Open 7, no. 8 (2024): e2429229.
  4. Assaf, Rida, Zein Shehabeddine, and Vikram Ramesh. "Screening autism spectrum disorder in children using machine learning on speech transcripts." Scientific Reports 15, no. 1 (2025): 34134.M. Elbattah, “Improving autism spectrum disorder diagnosis using machine learning techniques,” Frontiers in Neuroinformatics, 2024.
  5. Raajitha, Kondalapuri, and Sudha Thatimakula. "A Systematic Approach to Autism Spectrum Disorder Diagnosis Using Optimized Machine Learning Models." Int. J. Comput. Eng. Res. Trends 12, no. 11 (2025): 16-24.
  6. Eslami, Taban, Vahid Mirjalili, Alvis Fong, Angela R. Laird, and Fahad Saeed. "ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data." Frontiers in neuroinformatics 13 (2019): 70.
  7. Rubio-Martín, Sergio, María Teresa García-Ordás, Martín Bayón-Gutiérrez, Natalia Prieto-Fernández, and José Alberto Benítez-Andrades. "Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing." Health Information Science and Systems 12, no. 1 (2024): 20.
  8. Thabtah, Fadi. "Machine learning in autistic spectrum disorder behavioral research: A review and ways forward." Informatics for Health and Social Care 44, no. 3 (2019): 278-297.
  9. Raj, Suman, and Sarfaraz Masood. "Analysis and detection of autism spectrum disorder using machine learning techniques." Procedia Computer Science 167 (2020): 994-1004.
  10. Sharif, Hamza, and Rizwan Ahmed Khan. "A novel machine learning based framework for detection of autism spectrum disorder (ASD)." Applied Artificial Intelligence 36, no. 1 (2022): 2004655.
  11. Thabtah, F. (2017). Autism Screening Adult [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5F019