Original scientific paper

https://doi.org/10.7906/indecs.23.6.3

Highly Accurate Hybrid Method for Attention Deficit Hyperactivity Disorder Classification Based on ANFIS-RFE-GWO

Deepika Deepika ; The NorthCap University, Department of Computer Science and Engineering, Gurugram, Haryana, India *
Shaveta Arora ; The NorthCap University, Department of Computer Science and Engineering, Gurugram, Haryana, India
Meghna Sharma ; The NorthCap University, Department of Computer Science and Engineering, Gurugram, Haryana, India

* Corresponding author.

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurobehavioral disorders, characterised by persistent patterns of inattention, impulsivity, and restlessness. This disorder significantly affects the personal, social, and academic development of individuals, with millions of children and adolescents worldwide experiencing its symptoms. Despite its high prevalence, accurate diagnosis is still a significant challenge for medical professionals, as distinguishing affected individuals from healthy controls is often complex. Many machine learning and deep learning approaches have been proposed for its diagnosis; the accuracy of ADHD diagnosis is still insufficient and needs further improvement. This study proposes a highly accurate hybrid framework to address this gap. The proposed method integrates the Recursive Feature Elimination technique to select the relevant and most significant feature subset, an Adaptive Neuro-Fuzzy Inference System to perform classification while handling inherent data uncertainty, and Grey Wolf Optimisation for hyperparameter tuning.
Cross-validation is further applied to ensure optimal feature subset selection and robust model performance. The proposed framework is evaluated using phenotypic data from the ADHD-200 dataset, which included 547 patients diagnosed with the disorder and 325 healthy controls. For performance benchmarking, the proposed model is compared with several conventional machine learning classifiers, including Random Forest Classifier, K-Nearest Neighbour, and Gradient Boosting Classifier etc. Experimental results demonstrate that the proposed model outperforms several state-of-the-art approaches, achieving a classification accuracy of 98,30%, sensitivity of 96,82%, and F1-score of 97,60%. These results highlight the model’s potential as a reliable and effective diagnostic tool for clinical decision support, performing accurate detection and reducing misdiagnosis.

Keywords

attention deficit hyperactivity disorder; adaptive neuro-fuzzy inference system; fuzzy logic; grey wolf optimisation; recursive feature elimination

Hrčak ID:

342121

URI

https://hrcak.srce.hr/342121

Publication date:

23.12.2025.

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