Machine Learning–Based Early Autism Risk Prediction Using Clinical and Behavioral Indicators

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 6, pp. 01–07, June 2026

https://doi.org/10.66710/ijersem.v2i6.1

This work is licensed under a Creative Commons Attribution 4.0 International License .

Machine Learning–Based Early Autism Risk Prediction Using Clinical and Behavioral Indicators

1A Mahendra, 2Koondla Manogna, 2Palagiri Nagayogeswari, 2Bhumireddy Varshitha

1Assistant Professor, Department of CSE, RGUKT, RK Valley, Kadapa, India.
2UG Scholar, Department of CSE, RGUKT, RK Valley, Kadapa, India.

Abstract

Early identification of Autism Spectrum Disorder (ASD) plays an important role in improving developmental outcomes through timely intervention and support. This study presents a machine learning–based framework designed to estimate autism risk levels using selected clinical and behavioral attributes. A structured dataset containing demographic information and health-related indicators such as epilepsy, anxiety, congenital conditions, and developmental factors was utilized to train predictive models. Feature importance analysis enabled identification of the most influential attributes contributing to risk estimation. A Random Forest classifier demonstrated strong predictive capability with high ROC–AUC performance, supporting reliable classification of low, moderate, and high-risk categories. The system integrates an interactive screening interface that assists in generating risk probability along with category-specific preventive guidance recommendations. The proposed framework contributes to accessible early screening support tools that can assist caregivers, educators, and healthcare professionals in recognizing potential developmental concerns and facilitating timely clinical evaluation pathways.

Keywords: Autism Spectrum Disorder, Machine Learning, Early Screening, Risk Prediction, Random Forest.

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DOI: 10.66710/ijersem.v2i6.1

Open Access • Peer Reviewed Article

References

  1. G. K. Mittal, K. S. Gill, D. Upadhyay, V. Singh, and S. Aluvala, "Applying Machine Learning for Autism Risk Evaluation Using a Decision Tree Classification Technique," 2024 2nd International Conference on Computer, Communication and Control (IC4), Indore, India, pp. 1–6, 2024. https://doi.org/10.1109/IC457434.2024.10486622
  2. A. Tandon, B. D. Mazumdar, M. K. Pal, P. K. Gupta, and N. K. Sahu, "Enhancing Early Autism Prediction Using Machine Learning Techniques," 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), Delhi, India, pp. 1–5, 2025. https://doi.org/10.23919/INDIACom66777.2025.11115668
  3. T. Arunprasath, M. Niranjana, S. M., B. P. V., M. P. Rajasekaran, and K. Ramaraj, "Prediction of Autism Spectrum Disorder Using Machine Learning," 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Krishnankoil, Tamil Nadu, India, pp. 1–6, 2024. https://doi.org/10.1109/INCOS59338.2024.10527777
  4. K. Suthendran, V. L. Kumar, K. D. D. Yadav, and S. Surya, "Ensemble-Based Machine Learning and Deep Learning Approaches for Autism Prediction," 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, pp. 346–351, 2025. https://doi.org/10.1109/ICICV64824.2025.11085665
  5. B. Kamala, K. S. Mahanaga Pooja, S. Varsha, and K. Sivapriya, "ML Based Approach to Detect Autism Spectrum Disorder (ASD)," 2021 4th International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, pp. 313–318, 2021. https://doi.org/10.1109/ICCCT53315.2021.9711826
  6. S. K. R. Naik, D. M., R. P. B., S. Prakash, and U. J. Royal, "Determination and Diagnosis of Autism Spectrum Disorder Using Efficient Machine Learning Algorithm," 2023 3rd International Conference on Intelligent Technologies (CONIT), Hubli, India, pp. 1–5, 2023. https://doi.org/10.1109/CONIT59222.2023.10205718
  7. U. R., S. S., and S. K. K. S., "Autism Spectrum Disorder Prediction Using Machine Learning," 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India, pp. 1–6, 2024. https://doi.org/10.1109/ADICS58448.2024.10533493
  8. L. Mertz, "Using AI and ML to Predict Autism Spectrum Disorder," IEEE Pulse, vol. 15, no. 3, pp. 11–15, 2024. https://doi.org/10.1109/MPULS.2024.3443489
  9. M. R. Ahmmad, H. Pantazopoulos, and S. H. Kothiya, "A Comprehensive Causal and Machine Learning Framework for Autism Spectrum Disorder Risk Prediction and High-Risk Subgroup Identification from Children with Chronic Health and Genetic Disorders in the United States," Research in Autism, vol. 131, p. 202826, 2026. https://doi.org/10.1016/j.reia.2026.202826
  10. Sankari and A. Kannammal, "Diagnosis of Autism Spectrum Disorder—Prediction and Analysis of Enhanced Classification Model Based on Machine Learning," in Elsevier eBooks, pp. 241–257, 2025. https://doi.org/10.1016/B978-0-443-30080-6.00018-3
  11. S. Irram and M. Suaib, "An Early Detection of Autism Spectrum Disorder Using Machine Learning," Computers in Biology and Medicine, vol. 198, Part B, p. 111238, 2025. https://doi.org/10.1016/j.compbiomed.2025.111238
  12. R. Muñoz-Terol, J. Peral, S. Amador, and D. Gil, "A Systematic Review of Machine Learning Techniques to Address Diagnosis and Treatment of Autism: Challenges and Opportunities," Heliyon, vol. 12, no. 1, p. e44359, 2026. https://doi.org/10.1016/j.heliyon.2025.e44359
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This article is part of Volume 2, Issue 6 (June 2026)

2026-06-31