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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Related Experiment Video

Updated: May 6, 2026

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
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A Machine Learning Approach to Reference Interval Estimation for Red Cell Parameters in a South and East Asian

Veera Sekaran Nadarajan1, Pavai Sthaneshwar2, Jia Qi Lim3

  • 1Department of Preclinical Sciences, Faculty of Medicine and Health Sciences, University Tunku Abdul Rahman, Kajang, Selangor, Malaysia.

Annals of Laboratory Medicine
|July 3, 2025
PubMed
Summary

Machine learning accurately estimates red cell parameter reference intervals by excluding iron deficiency and hemoglobinopathies. This indirect approach provides results comparable to direct methods, improving accuracy and enabling age- and sex-specific ranges.

Keywords:
AnemiaAsiaBlood cell countMachine learningReference intervalSoutheastern

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Area of Science:

  • Hematology
  • Medical Diagnostics
  • Machine Learning in Healthcare

Background:

  • Iron deficiency and hemoglobinopathies are common in Southeast Asia, complicating accurate reference interval (RI) estimation for red cell parameters.
  • Excluding affected individuals from reference populations is challenging for direct RI determination.
  • Machine learning offers a potential solution for indirect RI estimation.

Purpose of the Study:

  • To develop and validate a machine learning model for indirect estimation of red cell parameter reference intervals.
  • To improve the accuracy of reference intervals in populations with high prevalence of iron deficiency and hemoglobinopathies.
  • To enable the generation of sex- and age-specific reference ranges.

Main Methods:

  • Developed an eXtreme Gradient Boosting (XGB) binary classification model to differentiate normal individuals from those with anemias.
  • Trained and validated the XGB model on complete blood count (CBC) datasets.
  • Estimated reference intervals (RIs) using the refineR algorithm on a subset of individuals predicted as normal by the XGB model.

Main Results:

  • The XGB model demonstrated high accuracy (AUC 0.97) in distinguishing normal from abnormal CBC results.
  • Approximately 62.9% of an independent dataset were predicted as normal, allowing for indirect RI estimation.
  • Indirectly derived reference limits (RLs) using XGB and refineR closely approximated directly derived RLs, particularly for hematocrit, hemoglobin, and red cell counts.

Conclusions:

  • Combining XGB and refineR provides an accurate indirect method for deriving red cell parameter reference intervals.
  • This approach yields results comparable to direct methods and overcomes challenges in excluding individuals with iron deficiency and hemoglobinopathies.
  • The indirect method facilitates the creation of sex- and age-specific reference ranges, which are difficult to obtain directly.