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Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and

Kadra Mohamed Abdillahi¹, Fatma Ceyla Eraldemir¹, Irfan Kösesoy²

¹Department of Biochemistry, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey.

Journal of Clinical Laboratory Analysis

|July 10, 2024

Summary

Machine learning redefined hemoglobin (Hb)A1c values for women with iron deficiency anemia (IDA) and diabetes. This improved glycemic interpretation, impacting clinical decisions for some patients.

Keywords:

diabetes mellitus hemoglobinA1c iron deficiency anemia machine learning

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Area of Science:

Endocrinology and Metabolism
Hematology
Medical Informatics

Background:

Iron deficiency anemia (IDA) complicates glycemic control in diabetic women.
Hemoglobin (Hb)A1c accuracy is crucial for diabetes management.
Erythrocyte and iron levels influence HbA1c measurements.

Purpose of the Study:

To redefine hemoglobin (Hb)A1c values using machine learning (ML) in women with IDA and diabetes.
To improve the accuracy of glycemic interpretation in this specific demographic.
To explore the interaction between erythrocytes, iron, and glycemic levels.

Main Methods:

Retrospective observational study of 17,526 adult women (2017-2022).
Classification of samples into diabetic, prediabetic, or non-diabetic groups.
Application of Support Vector Machines, Linear Regression, Random Forest, and K-Nearest Neighbor ML algorithms to predict HbA1c.

Main Results:

A 0.1 unit change in HbA1c values was observed.
This change led to altered clinical decisions for some patients.
ML models successfully predicted HbA1c values in IDA samples.

Conclusions:

ML analysis of HbA1c in women with IDA can reveal distinctions near critical thresholds.
This approach enhances precision in medical decision-making.
The intersection of technology and laboratory science offers promise for improved patient care.