Using classification techniques for statistical analysis of Anemia

Meena, K.; Tayal, Devendra K.; Gupta, Vaidehi; Fatima, Aiman

doi:10.1016/j.artmed.2019.02.005

Cited by 31 publications

(10 citation statements)

References 16 publications

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“…Anemia is a condition characterized by a reduction in the number of functional red blood cells or hemoglobin, the protein responsible for oxygen transportation [ 1 , 2 ]. Iron deficiency is the most common cause of anemia; however, the causes can be multifactorial, such as parasitic infestations, malaria, inflammation, hemoglobinopathies, as well as renal disease [ 3 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Anemia in women of reproductive age in Ecuador: Data from a national survey

Sosa-Moreno¹,

Reinoso-González²,

Méndez³

2020

PLoS ONE

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Background Anemia is a condition characterized by a decline in the number of functional red blood cells or hemoglobin. Women of reproductive age from low-and middle-income countries are at higher risk of anemia, which could lead to prenatal, obstetric and perinatal complications. The aim of our study was to explore associations between anemia status and a set of demographic, socioeconomic and reproductive factors, among Ecuadorian women of reproductive age (WRA). Methods We used data from non-pregnant, WRA (�12 and �49 years) women enrolled in the nationally representative cross-sectional Ecuadorian National Health and Nutrition Survey 2012 (ENSANUT-ECU 2012). Anemia and moderate-severe anemia were assessed using hemoglobin concentrations cutoffs of <12 g/dL and <11 g/dL, respectively. Logistic regression was used to obtain unadjusted and adjusted prevalence odds ratios (aOR). All analyzes were adjusted for multi-stage sampling, stratification and clustering. Results The study population included a subset of 7415 non-pregnant WRA. Mean hemoglobin concentration was 12.84 g/dL (95% CI = 12.8-12.9). The overall prevalence of anemia and moderate-severe anemia was 16.8% and 5.0%, respectively. Some factors were associated with an increase in anemia prevalence odds: living in Guayaquil (aOR 1.82, 95% CI 1.16-2.84) and Quito (aOR 1.84, 95% CI 1.17-2.90) compared to living in the rural Amazon, having given birth to more than four alive children compared with being nulliparous (aOR 1.85, 95% CI 1.00-3.43), currently taking contraceptives compared with former use (aOR 1.46, 95% CI 1.09-1.97). In addition, moderate-severe anemia was associated with age and region of residence. Conclusion In 2012, the prevalence of anemia among Ecuadorian WRA was considered a mild public health concern. However, we identified groups with higher anemia prevalence. Thus,

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Section: Introductionmentioning

confidence: 99%

Anemia in women of reproductive age in Ecuador: Data from a national survey

Sosa-Moreno¹,

Reinoso-González²,

Méndez³

2020

PLoS ONE

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“…Machine learning classification tools were also used to estimate the risk of non-infectious disease health outcomes. For example, studies have focused on estimating anaemia risk in children using standardised household survey data, 43 identifying children with the greatest risk of missing immunisation sessions, 44 and detecting high-risk births using cardiotocography data. 45 A study from Brazil aimed to assess the behavioural risk classification of sexually active teenagers.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

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“…MLP Classifier trains iteratively because the partial loss function derivatives are computed for updating the parameters in conjunction with model parameters in any step. It may also be applied to the loss function with a regularization time that reduces configuration parameters to avoid overfitting [31,32].…”

Section: Figure 2 Architecture Of the Proposed Network Model For Mlp Classificationmentioning

confidence: 99%

Identification of Sickle Cell Anemia Using Deep Neural Networks

et al. 2021

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A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF

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Using classification techniques for statistical analysis of Anemia

Cited by 31 publications

References 16 publications

Anemia in women of reproductive age in Ecuador: Data from a national survey

Anemia in women of reproductive age in Ecuador: Data from a national survey

Artificial intelligence and the future of global health

Identification of Sickle Cell Anemia Using Deep Neural Networks

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