Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4261 2022 2022 Information sciences department , College of life sciences , Kuwait University, Kuwait Safaa Safaa Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt; Jadara Research Center, Jadara University, Irbid 21110, Jordan El-Sayed M. El El-Kenawy The growing prevalence and clinical complexity of Obsessive–Compulsive Disorder (OCD) motivate the need for reliable, data-driven decision-support systems capable of improving diagnostic accuracy and robustness beyond traditional assessment methods. In this study, we propose an optimized deep learning framework that integrates a Deep Learning framework distilled by Gradient Boosting Decision Trees (DeepGBM) with a novel metaheuristic optimizer, the Ninja Optimization Algorithm (NiOA), to enhance OCD-related classification using structured demographic and clinical data. The main contribution of this work lies in the design of a unified optimization pipeline in which NiOA is employed for automated hyperparameter tuning of DeepGBM, and in the comprehensive comparison of this approach against baseline deep learning models and alternative metaheuristic optimizers, including Multiverse Optimization (MVO), Bat Algorithm (BA), and Particle Swarm Optimization (PSO). Experimental evaluation demonstrates that, at the baseline stage, DeepGBM outperforms Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Bidirectional Long Short-Term Memory networks (BiLSTM), achieving an accuracy of 0.8970 and an F-score of 0.8935. Following optimization, the proposed NiOA+DeepGBM framework achieves substantial performance gains, reaching an accuracy of 0.9779, sensitivity of 0.9763, specificity of 0.9793, and an F-score of 0.9770, consistently surpassing MVO+DeepGBM, BA+DeepGBM, and PSO+DeepGBM across all evaluation metrics. These results confirm the superior capability of NiOA in navigating complex hyperparameter spaces and enhancing both predictive accuracy and generalization. The implications of this work are significant for intelligent mental health assessment, as the proposed NiOA-optimized DeepGBM model offers a robust, clinically relevant decision-support tool that can assist clinicians in improving diagnostic reliability, reducing uncertainty, and supporting the development of scalable, AI-driven mental healthcare systems. 2026 2026 58 86 10.54216/JAIM.110103 https://www.americaspg.com/articleinfo/28/show/4261