Rheumatoid arthritis (RA)—a chronic, inflam-matory disease—causes bone as well as joint erosion, and ifuntreated, it can lead to patients’ disabilities. Early detectionof RA can have a key role in prognosis of the disease. Objectives: We aimed to develop an eXplainable Decision Sup-port System (XDSS) to assist primary care providers in earlydetection of patients with RA. Methods: Based on the Sparse Fuzzy Cognitive Maps andquantum-learning algorithm, we developed our explainable in-telligent system—which is available as a web server—to assistin the detection of RA patients at early stages and classify theseverity of their disease into six different levels, collaboratingwith two specialists in rheumatology and orthopedic surgery. We collected anonymous data of real patients from Shohada Univer-sity Hospital, Tabriz, Iran and used for model development. We also compared the results of our model with machine learning methods (e.g., linear discriminant analysis, Support Vector Ma-chines, and K-Nearest Neighbours). The weights obtained fromour model were saved and deployed as part of a web app to give risk intensity scores based on the patient information. Results and Conclusions:Our proposed model not only outper-formed other machine learning methods in terms of accuracy butalso, in contrast to the others, our model revealed the relationof the features with one another and gave higher explainability. For future studies, we are suggesting scaling up the developedapp and identifying facilitators and barriers of using this app in clinical practice.

The website can be accessed from: https://rahimislab.ca/ra-dss

Please cite this paper if you use this methodology or our code:

@article{rahimi2022quantum,
  title={Quantum-Inspired Interpertable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset},
  author={Rahimi, Samira Abbasgholizadeh and Kolahdoozi, Mojtaba and Mitra, Arka and Salmeron, Jose L and Navali, Amir Mohammad and Sadeghpour, Alireza and Mir Mohammadi, Amir},
  journal={Mathematics},
  volume={10},
  number={3},
  pages={496},
  year={2022},
  publisher={Multidisciplinary Digital Publishing Institute}
}