An Arabic Intelligent Diagnosis Assistant for Psychologists using Deep Learning

Mental illnesses have increased in recent years, especially after Covid-19 pandemic. In Saudi Arabia, the number of psychiatric clinics is small compared to the population density. As a result, psychologists encounter a variety of difficulties at work. The main goal of the current research is to develop a system that assists psychologists in the diagnosis process, which will be based on the DSM-5 (Diagnosis and Statistical Manual of Mental Disorders). The work on this research started with collecting the requirements and identifying users’ needs. In this matter, several interviews have been conducted with Saudi Psychologist and then a questionnaire was developed and distributed to psychologists in Saudi Arabia. Following an analysis of the needs and requirements, the system was designed. A deep learning technique was applied during the diagnosing process to address the issues mentioned by psychologists. Additionally, the proposed system helps psychologists by quickly calculating the results of psychological tests. The system was built as a website. The Convolutional Neural Network (CNN) algorithm was used with 96% accuracy to automatically predict the appropriate diagnosis and suggest the most suitable psychological test for the patient to take. System testing and usability testing were also conducted by involving patients and Saudi psychologists to test the usability of the system and the accuracy of the CNN model. The results indicate that the diagnosis prediction was accurate, and that each activity was completed faster. This demonstrated the model's high degree of accuracy and the system's interfaces' clarity. Additionally, psychologists' comments were encouraging and positive.

@article{Alayed2023,
  title     = {An Arabic Intelligent Diagnosis Assistant for Psychologists using Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Asmaa Alayed and Manar Alrabie and Sarah Aldumaiji and Ghaida Allhyani and Sahar Siyam and Reem Qaid},
  doi       = {10.14569/IJACSA.2023.0140634},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140634}
}