AnaDetect: An extensive dataset for advancing anemia detection, diagnostic methods, and predictive analytics in healthcareMendeley Data

Abstract

Anemia is a critical medical condition in public health concern in tropical and subtropical areas, and understanding its hematological changes is crucial for improving diagnosis, treatment, and prognosis.It manifests through symptoms like weakness, fatigue, pale skin, and shortness of breath due to insufficient hemoglobin or red blood cells to carry adequate oxygen, with severe cases leading to complications such as chest pain. Common causes include blood loss, chronic diseases, and iron and vitamin deficiencies. This dataset captures various hematological parameters of patients suffering from anemia, including sex, age, Hemoglobin level (Hb), oxygen transportation (RBC), packed cell volume (PCV), mean corpuscular volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean corpuscular hemoglobin concentration (MCHC). The data is systematically collected from patients admitted to Aalok Healthcare Ltd., situated in Dhaka, Bangladesh., offering an opportunity to analyze hematological variations in patients with anemia. By providing a worldwide viewpoint for comparing hematological responses, this dataset aids in the development of prediction models for the severity of anemia and patient outcomes, improving clinical decision-making. The study examines how various treatment plans can affect blood characteristics, potentially leading to improved treatment strategies. For statistical analysis, the data is cleaning the noise (null and duplicate values), normalized, and encoded. The Chi-square test results indicate a p-value of 4.1929×10−29, showing no significant association between gender and diagnostic outcomes. However, the Z-test and T-test results reveal a notable gender difference in hemoglobin levels, with p-values of 3.4789×10−33 and 4.1586×10−24, respectively, underscoring the relevance of gender in analyzing hemoglobin variations. These findings emphasize how gender influences hematological responses against Anemia. The dataset will greatly advance research on anemia, improve these critical medical terms in public health strategies, and enhance patient diagnosis and treatment methods, offering a distinct advantage.

Keywords

• Blood disorder
• Hematological parameters
• Predictive models
• Clinical decision-making
• Gender influence
• Blood analysis