Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification

Kılıçarslan, Serhat; Çelik, Mete; Şahin, Şaziye

doi:10.1016/j.bspc.2020.102231

Cited by 69 publications

(30 citation statements)

References 35 publications

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“…Estudos anteriores mostraram que o conhecimento sobre a nutrição dos pacientes por médicos está relacionado a um melhor comportamento de cuidados de saúde. O desenho do estudo, a localização e as variáveis usadas para determinar o conhecimento sobre a anemia podem contribuído para os estudos (Chaparro;Suchdev, 2019;Kilicarslan;Celik;Sahin, 2021).…”

Section: Discussionunclassified

Conhecimento acerca de anemias e suas diferenciações entre estudantes de Medicina do Piauí

Silva

Carvalho

Almeida

2021

RSD

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Considerando que o acerto tempestivo do diagnóstico de uma doença por parte do profissional médico aumenta as chances de cura mais rápida e contribui para não haver agravamentos (sequelas), considerando que haver diversos tipos de anemias e o acometimento cada vez mais frequente nos indivíduos torna-se primordial avaliar o conhecimento a respeito do tema entre profissionais em formação. Este estudo buscou analisar comparativamente o conceito de anemias, seus tipos e diagnósticos diferenciais entre os internos de medicina de uma faculdade do Piauí, através de um formulário com questionamentos a respeito de conceitos, tipos e diagnósticos diferenciais entre anemias. Obtendo dados positivos sobre o nível de conhecimentos dos alunos, contudo não há muitas publicações cientificas semelhantes, o que limita a comparação de resultados, todavia, evidenciou-se que existe margem para melhora, uma vez que o tema é bastante abrangente e importante em um país com tamanha diversidade e desigualdades sociais como brasil.

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Section: Discussionunclassified

Conhecimento acerca de anemias e suas diferenciações entre estudantes de Medicina do Piauí

Silva

Carvalho

Almeida

2021

RSD

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“…Literature [14] holds that because there is no scientific budget accounting system at the national level, researchers and financial personnel have no calculation basis, so they can only calculate by experience, which leads to the incomplete and untrue content of budget preparation and cannot well reflect the research cost of the whole process of SR. Literatures [15,16] hold that there are two problems in the performance of university SR projects: first, the lack of standards for performance evaluation of SR projects makes the overall performance evaluation unable to be effectively reflected; second, the lack of professionalism in SR content in the performance appraisal group leads to the lack of persuasiveness in performance appraisal results. Literature [17] holds that the person in charge of SR should establish the consciousness of budget management and attach importance to the role of budget in the management of SR funds. Literature [18] holds that internal control should be taken as the starting point to improve the management level of SR projects and the use efficiency of funds.…”

Section: Related Workmentioning

confidence: 99%

Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

2022

Scientific Programming

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In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

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“…At the end of the classification process, the data were divided into 3 classes: HGB-anemia, nutritional anemia, and patients without anemia. They enriched the stacked autoencoder and CNN methods, deep learning algorithms, genetic algorithms, and successful results [41]. Yeruva et al, in their study, determined the type of deterioration of hemoglobin structure from the shapes of red blood cells using deep neural network methods.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

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Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

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Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification

Cited by 69 publications

References 35 publications

Conhecimento acerca de anemias e suas diferenciações entre estudantes de Medicina do Piauí

Conhecimento acerca de anemias e suas diferenciações entre estudantes de Medicina do Piauí

Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

Diagnosing Hematological Disorders Using Deep Learning Method

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