Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron-Deficiency Anemia Using Machine Learning Algorithms.

Journal: Journal of clinical laboratory analysis

PMID: 38984861

Abstract

OBJECTIVE: In response to the nuanced glycemic challenges faced by women with iron deficiency anemia (IDA) associated with diabetes, this study uses advanced machine learning algorithms to redefine hemoglobin (Hb)A1c measurement values. We aimed to improve the accuracy of glycemic interpretation by recognizing the critical interaction between erythrocytes, iron, and glycemic levels in this specific demographic group.

Authors

Kadra Mohamed Abdillahi

Department of Biochemistry, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey.
Fatma Ceyla Eraldemir

Department of Biochemistry, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey.
İrfan Kösesoy

1 Department of Computer Engineering, Yalova University , Yalova, Turkey .

Keywords

Adult Aged Algorithms Anemia, Iron-Deficiency Blood Glucose Diabetes Mellitus Female Glycated Hemoglobin Humans Machine Learning Middle Aged Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38984861)

Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron-Deficiency Anemia Using Machine Learning Algorithms.

Abstract

Authors

Keywords

External Resources

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