Analysis of Anemia Using Data Mining Techniques with Risk Factors Specification

Mohammed, Mohammed Sami; Ahmad, Arshed A.; Sarı, Murat

doi:10.1109/incet49848.2020.9154063

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…Research by Kobayashi et al [26] the study examination of anemia using a hyperspectral camera, gave results of two measurements at the level of pigmentation that bands 510-600 nm. Unlike others, research by Tamir et al [27] showing severe anemia, the detection of amenia from the image of the anterior eye, in the noninvasive procedure taken with a smartphone camera that detects conjunctival pallor, gave a result with an accuracy of 78.9%, whereas the results of [25] are more accurate and closer to our results. On the contrary, according to Jayakody and Edirisinghe [28] mobile application for the detection of anemia based on autonomous learning with neural networks entails that the user must answer a questionnaire in which he obtains an approximate result, maintaining the similarity of discard and acceleration with the presented study, unlike the study [29] to determine the morphological classification of anemia based on algorithms that classify the type of sickle cell disease through machine learning generating an average probability of detecting the type and accelerating the diagnosis as in Table 6.…”

Section: Key Performance Indicator (Kpi) 2: "Number Of Diagnoses"supporting

confidence: 73%

“…After having analyzed, it is observed that the p-value is less than 0.05 and this means that the mobile application positively influences the diagnosis of anemia in children in times of pandemic. Research by Mohammed et al [25] which studied the analysis of anemia by data mining, it is explained that 4 methods were applied for the measurement, of which logistic regression (LR) and multilayer perceptron (MLP) showed 87.3% and 87.1% efficiency, respectively, in contrast to other results. Research by Kobayashi et al [26] the study examination of anemia using a hyperspectral camera, gave results of two measurements at the level of pigmentation that bands 510-600 nm.…”

Section: Key Performance Indicator (Kpi) 2: "Number Of Diagnoses"mentioning

confidence: 99%

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Mobile application for the control process of childhood anemia in time of the pandemic

Mancisidor-Bazán,

Morales-Guillén,

Cabanillas-Carbonell

2023

IJPHS

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The objective of the research was to implement a mobile application for the control process of childhood anemia in times of pandemic, in order to have better medical control with faster care, better control of the diagnosis of anemia in children, and better performance and satisfaction. The following Scrum method was applied. The research has a quantitative approach, of experimental type. The sample consisted of 40 children. Results were obtained regarding time control in the speed of care, an increase of 10.2% was obtained; regarding the number of diagnoses, control, and follow-up in recovery there was an increase of 33.3% and finally regarding the control of performance and satisfaction with care there was an increase of 73.3%.

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Section: Key Performance Indicator (Kpi) 2: "Number Of Diagnoses"supporting

confidence: 73%

Section: Key Performance Indicator (Kpi) 2: "Number Of Diagnoses"mentioning

confidence: 99%

Mobile application for the control process of childhood anemia in time of the pandemic

Mancisidor-Bazán,

Morales-Guillén,

Cabanillas-Carbonell

2023

IJPHS

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“…M. S. MOHAMMED et al [9] specify Data Mining for the analysis of anemia. Based on available data, anemia has been predicted using four techniques: Bayesian Network (BN), Naive Bayes (NB), Logistic Regression (LR), and Multilayer Perceptron (MLP).…”

Section: Related Workmentioning

confidence: 99%

Web Mining and Anemia Classification in Gujarat via WWW

Thakor,

Parikh,

Dabhi

et al. 2023

SMSJ

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Web mining makes extracting internet data from big databases feasible. Low hemoglobin or insufficient red blood cells are symptoms of anemia. Undernutrition is the most common cause, and it is most common in young children, pregnant women, and women of childbearing age. If it is not treated or doesn't take into consideration, it could lead to an enlarged heart or heart failure. In this study, we gather data from the web about anemia in Gujarat and categorize it in various ways based on its parameter. The dataset builds from web scrapping using Python soup. The dataset we extract can be further classified to make a decision. This research helps in identifying anemia-related information as well as saving lives. Data mining tools predict future trends, help organizations, and present knowledge in the form of the easiest way so that humans can understand. The data can be processed, analyzed, and presented by a specifically trained algorithm for diverse stakeholders, including policymakers, program planners, health service providers, academicians, research scholars, and common citizens, enabling them for evidence-based, data-driven decision-making

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“…Reference [15] employs data mining techniques to predict anemia, comparing Bayesian Network, Naive Bayes, Logistic Regression, and Multilayer Perceptron. Logistic Regression and Multilayer Perceptron demonstrate high performance, with the study highlighting their efficiency in predicting anemia.…”

Section: IImentioning

confidence: 99%

Machine Learning Driven Anemia Identification and Classification: A Comprehensive Survey

Dhengre,

Patil,

Mukherjee

et al. 2023

IJRASET

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Anemia, a common medical condition characterized by a deficiency in red blood cells or hemoglobin, presents a significant health concern globally. The development of accurate and efficient tools for the identification and classification of anemia is crucial for early diagnosis and targeted intervention. Machine learning, with its robust capabilities in pattern recognition, has emerged as a promising avenue for addressing this challenge. This literature survey paper provides a comprehensive overview of existing research in the field of anemia identification and classification using machine learning. The paper begins by delineating the epidemiological significance of anemia, underlining the pressing need for advanced diagnostic techniques. A systematic examination of the physiological basis of anemia, its types, and clinical symptoms sets the stage for understanding the complexity of anemia classification. We review traditional diagnostic methods and their limitations, emphasizing the potential for machine learning to augment and enhance these techniques. The primary objective of this survey is to synthesize existing knowledge in the field, identify common trends, and highlight advancements that have significantly contributed to the accuracy and efficiency of RBC classification methods. The survey is organized into several key sections, each exploring distinct aspects of previous research endeavors. The survey commences with an overview of the importance of RBC classification in medical diagnostics, emphasizing the critical role of automated techniques in improving efficiency and accuracy. It then delves into the fundamentals of image processing and CNNs, providing a foundation for understanding the methodologies discussed in subsequent sections. The core of the survey is dedicated to a comprehensive analysis of previous implementations, categorizing them based on their proposed models and datasets. Each section provides a detailed review of the methodology, highlighting the key contributions and innovations, and the datasets used for training and evaluation. Specific areas of focus include feature extraction techniques, network architectures, and classification algorithms employed in these projects. Furthermore, the survey addresses challenges encountered in RBC classification, such as dataset scarcity, class imbalance, and the need for robust and interpretable models. It also explores the potential for transfer learning and the integration of emerging technologies like deep learning and edge computing in this domain. In conclusion, this paper not only illuminates the advancements made in anemia detection but also underscores the gaps and opportunities for further research. It provides a valuable resource for researchers, medical practitioners, and data scientists seeking to enhance the accuracy and efficiency of RBC classification systems, ultimately contributing to the improvement of medical diagnostics and patient care.

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Analysis of Anemia Using Data Mining Techniques with Risk Factors Specification

Cited by 17 publications

References 26 publications

Mobile application for the control process of childhood anemia in time of the pandemic

Mobile application for the control process of childhood anemia in time of the pandemic

Web Mining and Anemia Classification in Gujarat via WWW

Machine Learning Driven Anemia Identification and Classification: A Comprehensive Survey

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