Determination of Women Iron Deficiency Anemia Using Neural Networks

Yilmaz, Ziynet; Bozkurt, Mehmet Recep

doi:10.1007/s10916-011-9772-4

Cited by 20 publications

(11 citation statements)

References 3 publications

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“…There is evidence that teenage girls are predominantly susceptible to iron-deficiency anemia. Similarly, studies in adult women show a correlation between IDA and fatigue (5).…”

Section: Introductionmentioning

confidence: 87%

“…Generally, IDA is classically designated as a microcytic anemia. Thalassemia trait (minor) is among the most common types of microcytic anemia (5).…”

Section: Introductionmentioning

confidence: 99%

“…The network involves a large number of interconnected processing elements (neurons), which perform in parallel distributed processing systems (5, 16).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, neural networks are widely used in medical diagnosis system (17). In order to reach an optimum solution and problem solving of variety of different disciplines, various network architecture and learning algorithm of ANN have been developed (5). Multilayer perception neural networks (MLPNNs) are nonparametric systems applied for the performance of wide range of detection and estimation tasks (5).…”

Section: Introductionmentioning

confidence: 99%

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Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes

2019

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Introduction: Iron deficiency anemia (IDA) and β-thalassemia trait (β-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and β-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. Aim: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and β-TT in comparison with traditional methods. Methods: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and β-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and β-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. Result: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with β-TT. Conclusion: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from β-TT and assist in accurate and timely manner diagnosis.

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“…There is evidence that teenage girls are predominantly susceptible to iron-deficiency anemia. Similarly, studies in adult women show a correlation between IDA and fatigue (5).…”

Section: Introductionmentioning

confidence: 87%

“…Generally, IDA is classically designated as a microcytic anemia. Thalassemia trait (minor) is among the most common types of microcytic anemia (5).…”

Section: Introductionmentioning

confidence: 99%

“…The network involves a large number of interconnected processing elements (neurons), which perform in parallel distributed processing systems (5, 16).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes

2019

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“…Notable among them is the works of [34], [35], [36], [37] , [38], etc. In the research of Pergialiotis et al [38], they investigated the diagnostic accuracy of three different techniques, that is, logistic regression, ANNs, and the Classical and Regression Tree(CART) for the prediction of endometrial cancer in postmenopausal women with vaginal bleeding or endometrial thickness 5mm, as determined by ultrasound examination. It is a case-control study based on data obtained from the analysis of pathology reports of curettage specimens in postmenopausal women.…”

Section: 3cancer Diseasementioning

confidence: 99%

Untitled

2020

IJCSES

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Artificial Intelligence systems (especially computer-aided diagnosis and artificial neural networks) are increasingly finding many uses in medical diagnosis application in recent times. These methods are adaptive learning algorithms that are capable of handling multiple and heterogeneous types of clinical data with a view of integrating them into categorized outputs. In this study, we briefly review and discuss the concept, capabilities, and applicability of artificial neural network techniques to medical diagnosis, through consideration of some selected physical and mental diseases. The study focuses on scholarly researches within the years, 2010 to 2019. Findings show that no electronic online clinical database exists in Nigeria and the Sub-Saharan countries, most review researches in this area focused mainly on physical diseases without considering mental illnesses, the application of ANN in mental and comorbid disorders have not been thoroughly studied, ANN models and algorithms consider mainly homogeneous input data sources and not heterogeneous input data sources, and ANN models on multi-objective output systems are few as compared to single output ANN models.

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SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition

Baraybar-Huambo

Gutiérrez-Cárdenas

2020

Information Management and Big Data

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Determination of Women Iron Deficiency Anemia Using Neural Networks

Cited by 20 publications

References 3 publications

Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes

Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes

Untitled

SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition

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