AI-Based Parkinson’s Disease Diagnosis and Prediction with Therapeutic Game Design for Engagement

Parkinson’s disease (PD) is a progressive neurodegenerative disease that impacts motor and cognitive functions, and early diagnosis and management are essential to enhance patient outcomes. The study assumes the implementation of Artificial Intelligence (AI)-based diagnostic and predictive algorithms, along with therapeutic game design, to assist patients in improving the management and treatment of PD. The existing approaches to PD diagnosis rely heavily on clinical observation of symptoms and on traditional imaging methods, which may be subjective, time-consuming, and prone to human error. Moreover, conventional interventions are not consistently engaging or tailored to the patient, and hence, treatment adherence is not optimal. To overcome these difficulties, we present PD in the framework of AI (PD-AI), leveraging machine learning algorithms to enhance early diagnosis and predict disease progression. The system will be implemented as a mobile app that integrates AI with therapeutic gaming, with real-time symptom tracking based on sensor readings (e.g., tremors, motor skills) and interactive therapeutic games provided to the patient to maintain their engagement. The suggested approach enhances early diagnosis rates, provides a tailored approach, supports continuous monitoring of symptoms, and encourages patients to follow their treatment actively. An active, efficient, and convenient management strategy is facilitated by data analysis based on frequent examinations and feedback via the app. Preliminary results indicate that the PD-AI model improves case diagnosis accuracy and patient compliance with treatment regimens, demonstrating its effectiveness for both medical experts and patients with PD.

@article{Almozani2026,
  title     = {AI-Based Parkinson’s Disease Diagnosis and Prediction with Therapeutic Game Design for Engagement},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Marwah Muwafaq Almozani and Hüseyin Demirel},
  doi       = {10.14569/IJACSA.2026.0170173},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170173}
}