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Classification of α-thalassemia data using machine learning models.
Comput Methods Programs Biomed
January 2025
Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
Background: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers.
View Article and Find Full Text PDFFructosamine as an Indicator for Glycemic Control in the Veteran Community.
Mil Med
December 2024
Department of Surgery, Podiatric Surgery, Stony Brook University Hospital, Stony Brook, NY 11794, USA.
Objective: Glycemic monitoring via Hemoglobin A1(HbA1c) proves to be inaccurate when a patient is diagnosed with a hemoglobinopathy/erythrocyte disorder. Moreover, any acute changes of glycemic intake within 6 weeks of blood sampling have been noted to impart a greater effect on HbA1c than the remaining days of the supposed overall 3-to-4-month average of glycemic control. Fructosamine, an alternative to HbA1c, allows physicians to analyze glycemic control in the presence of an underlying hemoglobinopathy/erythrocyte disorder.
View Article and Find Full Text PDFPerformance Evaluation of Commonly Used Portable Hemoglobin Sensors in Comparison to Clinical Test Results.
Stud Health Technol Inform
August 2024
Kyushu University, Fukuoka, Japan.
Telehealth systems in underserved countries leverage various low-cost portable medical sensors to transmit patients' vital information to remote doctors, facilitating timely diagnoses and interventions. However, the potential risks associated with inaccurate data pose considerable threats to the health of individuals. This study focuses on identifying high-quality portable hemoglobin sensors, employing the Japanese clinical pathology laboratory as a gold standard.
View Article and Find Full Text PDFData-driven rapid detection of infection through machine learning with limited laboratory parameters in Chinese primary clinics.
Heliyon
August 2024
Department of Spleen and Gastroenterology, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China.
Background: is a significant global health concern, posing a high risk for gastric cancer. Conventional diagnostic and screening approaches are inaccessible, invasive, inaccurate, time-consuming, and expensive in primary clinics.
Objective: This study aims to apply machine learning (ML) models to detect infection using limited laboratory parameters from routine blood tests and to investigate the association of these biomarkers with clinical outcomes in primary clinics.
Emerging Evidence and Critical Issues with the Use of Single-Drop Capillary Blood for the Measurement of Hemoglobin Concentration in Population-Level Anemia Surveys.
Adv Nutr
October 2024
Program Operations Unit, Nutrition International, Ottawa, Ontario, Canada. Electronic address:
Accurate and precise measurement of hemoglobin concentration is critical for reliable estimations of anemia prevalence at the population level. When systematic and/or random error are introduced in hemoglobin measurement, estimates of anemia prevalence might be significantly erroneous and, hence, limit their usefulness. For decades, single-drop capillary blood has been the most common blood source used for the measurement of hemoglobin concentration in surveys, especially in low-income and middle-income countries.
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