Identifying Severity Clusters in SLE Patients

Hamza Zidoum*, Sumaya AL-Sawafi, Aliya AL-Ansari, Batool AL-Lawati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Machine learning (ML) has a successful impact in healthcare data mining. We use unsupervised ML methods to extract features and identify subgroups of Systemic Lupus Erythematosus (SLE) patients related to the disease severity. We analyze the similarity between SLE patients within these clusters. Finally, we evaluate the clustering results, using two types of cluster validation, internal cluster validation, and external cluster validation. The clustering analysis results show two separate patients clusters which are mild and severe subgroups. Patients in the severe subgroup have a higher prevalence of the renal disorder, hemolytic anemia, anti-dsDNA anti- body, and low complements (C3, C4). The severe subgroup of patients suffer from malar rash and proteinuria with higher use of cyclophosphamide, mycophenolate mofetil, and azathioprine. The second cluster is mild disease activity, and it is associated with joint pain, low complements (C3, C4), and a positive anti-dsDNA antibody.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2022, Volume 3
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages19
ISBN (Print)9783031183430
Publication statusPublished - 2023
Event7th Future Technologies Conference, FTC 2022 - Vancouver, Canada
Duration: Oct 20 2022Oct 21 2022

Publication series

NameLecture Notes in Networks and Systems
Volume561 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference7th Future Technologies Conference, FTC 2022


  • Biomedical informatics
  • Clustering
  • Data analytics
  • Healthcare
  • Systemic Lupus Erythematosus (SLE)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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