Diagnosis of rheumatic and autoimmune diseases dataset.

Journal: Data in brief
Published Date:

Abstract

This study presents a primary dataset collected from one hospital and three laboratories in Iraq between 2019 and 2024. The dataset includes both the case and control groups, the case group comprising patients diagnosed with six common rheumatic and autoimmune diseases: rheumatoid arthritis, reactive arthritis, ankylosing spondylitis, Sjögren syndrome, systemic lupus erythematosus, and psoriatic arthritis. The dataset contains records of 12,085 patients with rheumatic and autoimmune diseases. Patient privacy is ensured through data anonymization. The dataset includes 14 features in seven classes, aiding the development of machine learning models for the early and accurate diagnosis of rheumatic and autoimmune diseases. This dataset is valuable for clinical decision support, remote healthcare, drug development, and medical diagnosis. It facilitates early diagnosis, supports explainable artificial intelligence models, advances precision medicine, enhances research, and reduces diagnostic costs and time.

Authors

  • Mohammed Fadhil Mahdi
    Faculty of Electrical and Computer Engineering, Sahand University of Technology, Tabriz, Iran.
  • Arezoo Jahani
    Faculty of Electrical and Computer Engineering, Sahand University of Technology, Tabriz, Iran.
  • Dhafar Hamed Abd
    College of Computer Science and Information Technology University of Anbar Ramadi, Iraq.

Keywords

No keywords available for this article.