Automated prediction of low ferritin concentrations using a machine learning algorithm

Steef Kurstjens*, Thomas De Bel, Armando van der Horst, Ron Kusters, Johannes Krabbe, Jasmijn van Balveren

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
95 Downloads (Pure)


Objectives: Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP).

Methods: Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool.

Results: Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day.

Conclusions: Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.

Original languageEnglish
Pages (from-to)1921-1928
Number of pages8
JournalClinical Chemistry and Laboratory Medicine
Issue number12
Early online date8 Mar 2022
Publication statusPublished - 4 Nov 2022


  • Artificial Intelligence (AI)
  • Case-finding
  • Diagnostics
  • Iron deficiency
  • Laboratory information system
  • Reflective testing
  • 2023 OA procedure


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