Quantum-Inspired Interpretable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset

Samira Abbasgholizadeh Rahimi, Mojtaba Kolahdoozi, Arka Mitra, Jose L. Salmeron, Amir Mohammad Navali, Alireza Sadeghpour and Seyed Amir Mir Mohammadi
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Samira Abbasgholizadeh Rahimi: Department of Family Medicine, McGill University, Montreal, QC H3A 0G4, Canada
Mojtaba Kolahdoozi: Department of Electrical Engineering, Iran University of Science and Technology, Tehran 1311416846, Iran
Arka Mitra: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
Jose L. Salmeron: Data Science Lab, Universidad Pablo de Olavide, Ctra. de Utrera km. 1, 41013 Sevilla, Spain
Amir Mohammad Navali: Tabriz University of Medical Sciences & Orthopedic Surgery Department, Shohada Medical Research & Training Hospital, East Azerbaijan, Tabriz 5165665931, Iran
Alireza Sadeghpour: Tabriz University of Medical Sciences & Orthopedic Surgery Department, Shohada Medical Research & Training Hospital, East Azerbaijan, Tabriz 5165665931, Iran
Seyed Amir Mir Mohammadi: Department of Family Medicine, McGill University, Montreal, QC H3A 0G4, Canada

Mathematics, 2022, vol. 10, issue 3, 1-19

Abstract: Rheumatoid arthritis (RA) is a chronic inflammatory and long-term autoimmune disease that can lead to joint and bone erosion. This can lead to patients’ disability if not treated in a timely manner. Early detection of RA in settings such as primary care (as the first contact with patients) can have an important role on the timely treatment of the disease. We aim to develop a web-based Decision Support System (DSS) to provide a proper assistance for primary care providers in early detection of RA patients. Using Sparse Fuzzy Cognitive Maps, as well as quantum-learning algorithm, we developed an online web-based DSS to assist in early detection of RA patients, and subsequently classify the disease severity into six different levels. The development process was completed in collaborating with two specialists in orthopedic as well as rheumatology orthopedic surgery. We used a sample of anonymous patient data for development of our model which was collected from Shohada University Hospital, Tabriz, Iran. We compared the results of our model with other machine learning methods (e.g., linear discriminant analysis, Support Vector Machines, and K-Nearest Neighbors). In addition to outperforming other methods of machine learning in terms of accuracy when all of the clinical features are used (accuracy of 69.23%), our model identified the relation of the different features with each other and gave higher explainability comparing to the other methods. For future works, we suggest applying the proposed model in different contexts and comparing the results, as well as assessing its usefulness in clinical practice.

Keywords: artificial intelligence; interpretable machine learning; fuzzy cognitive maps; rheumatoid arthritis; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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