Enhanced Autism Severity Prediction: Hybrid Gradient Boosted Tree and Deep Learning Models

Authors

  • R. Ramya, S. Panneer Arokiaraj

Keywords:

Autism Historical Dataset, Autism Spectrum Disorder (ASD), Data Science Models, Hybrid for Gradient Boosted Tree and Deep Learning (GBT-DL).

Abstract

Autism Spectrum Disorder (ASD) is a condition of developmental disability impacting both behavior and brain functionality.  It cannot be diagnosed through medical tests; hence, the diagnosis relies heavily on historical data. Data science models, like Gradient Boosted Trees and Deep Learning, play a crucial role in predicting autism risk by evaluating relevant information and identifying patterns. This paper proposes a novel Hybrid Model that combines the advantages of both Gradient Boosted Tree and Deep Learning models. The aim is to reduce the number of necessary diagnostic tests for autism, thereby offering potential solutions for the healthcare sector. This model achieved an accuracy of 95.52% in predicting the severity of autism using historical adult autism data. The historical patient data used for this study is available on the Kaggle Repository. This perspective highlights the crucial importance of data science in diagnosing healthcare issues.

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Published

12.06.2024

How to Cite

R. Ramya. (2024). Enhanced Autism Severity Prediction: Hybrid Gradient Boosted Tree and Deep Learning Models . International Journal of Intelligent Systems and Applications in Engineering, 12(4), 1043 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6341

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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