2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 2 , PP: 53-64, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Awad Alasmrai 1 , Ramadan Mohamed Ismail 2 * , Mohammed Hasan Ali Al-Abyadh 3
Doi: https://doi.org/10.54216/FPA.200205
This paper presents a novel machine-learning framework designed to personalize Cognitive Behavioral Therapy (CBT) for adult patients by leveraging a multi-dimensional, adaptive approach. The proposed system integrates historical clinical data, real-time behavioral indicators, and contextual factors to generate a comprehensive psychological profile for each adult patient. A reinforcement learning mechanism underpins therapy selection, allowing the model to iteratively refine treatment strategies based on individual responses and therapeutic outcomes. An embedded optimization process enables dynamic adaptation of interventions, improving predictive accuracy and fostering patient-centered care. The framework incorporates a multi-factor assessment model that synthesizes psychological, behavioral, and physiological variables to enhance therapeutic effectiveness, sustainability, and responsiveness to change. Comparative evaluations demonstrate that this approach outperforms traditional CBT planning methods, as well as existing deep learning, hybrid, and reinforcement-based models, in terms of accuracy, interpretability, computational efficiency, and patient outcome optimization for adults. Furthermore, the system emphasizes fairness and equity in treatment personalization, supporting real-time clinical decision-making while minimizing ineffective therapeutic pathways. This research underscores the transformative potential of machine learning in mental health care by enabling scalable, data-driven, and continuously improving interventions tailored to the nuanced needs of adult patients undergoing CBT.
Adaptive learning , Adult Cognitive behavioral therapy , Data-driven therapy , Iterative optimization , Machine learning , Mental healthcare , Patient-centered outcomes , Personalization , Reinforcement learning , Therapy recommendation
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