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Proceedings ArticleDOI

Blood Transfusion System Using Data Mining Techniques and GRA

15 Mar 2019-pp 1143-1147
TL;DR: This work has applied and compared various algorithms such as Decision tree, Random forest and logistic regression, before and after applying GRA to develop a system for predicting the nature of a blood donor, based on past data of donation.
Abstract: We are suggesting a blood transfusion system based on various data mining algorithms. We have applied and compared various algorithms such as Decision tree, Random forest and logistic regression, before and after applying GRA. The algorithm with most efficiency will be used to develop a system for predicting the nature of a blood donor, based on past data of donation. The prediction will be useful at various blood donation camps for contacting the donors, in case of a requirement.
Citations
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Proceedings ArticleDOI
26 Jun 2021
TL;DR: In this paper, an effective method of contacting donors, that can be useful in an emergency is suggested, when a person requires blood, they request it through a website or mobile device; the request is then routed to the person who meets the matching blood type.
Abstract: Blood is one of the most vital and essential elements in human existence. When the population increases, so do the request for blood. People who need blood in an emergency are unable to provide it promptly. This paper suggests an effective method of contacting donors, that can be useful in an emergency. When a person requires blood, they request it through a website or mobile device; the request is then routed to the person who meets the matching blood type. the application is then sent an SMS to the donor to approve it, after accepting the request, the application will inform the requestor about it and he/she will get the donor phone number by using an MCU ESP8266 and a SIM800L. The privacy of the person must be protected in the current environment. The donor will be deleted from the reception of notification for the next three months after the blood donation is done. User name, password, and phone number are used to check registered accounts.

2 citations

Proceedings ArticleDOI
07 Jun 2022
TL;DR: In this paper , a revisão sistemática da literatura sobre o uso de técnicas de aprendizado de máquina that ajudem a obter uma melhor compreensão sobre os fatores that influenciam o comportamento futuro do doador to poder prevê-lo com maior precisão and assim definir estratégias for convocar and aumentar o número of doadores ativos.
Abstract: Serviços de Hemoterapia, chamados comumente de Unidades Hemoterápicas, muitas vezes tem problemas para convocar doadores de sangue em épocas de crise ou em situações de emergência como a recente pandemia do COVID-19. Além disso, nesses serviços existe uma preocupação constante em os estoques de sangue em patamares seguros e aceitáveis. Este artigo apresenta uma revisão sistemática da literatura sobre o uso de técnicas de aprendizado de máquina que ajudem a obter uma melhor compreensão sobre os fatores que influenciam o comportamento futuro do doador para poder prevê-lo com maior precisão e assim definir estratégias para convocar e aumentar o número de doadores ativos. Foram revisados 17 artigos, selecionados dos 171 artigos recuperados inicialmente de 5 bases indexadas. O artigo, também, discorre sobre os 10 trabalhos considerados mais relevantes para o entendimento do comportamento dos doadores.
References
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Journal ArticleDOI
TL;DR: This work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
Abstract: An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier - especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.

2,672 citations


"Blood Transfusion System Using Data..." refers methods in this paper

  • ...Bagging is used for such purposes and for random selection from existing training set [7]....

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Journal ArticleDOI
01 Aug 1996-Synthese
TL;DR: F fuzzy logic is used in this paper to describe an imprecise logical system, FL, in which the truth-values are fuzzy subsets of the unit interval with linguistic labels such as true, false, not true, very true, quite true, not very true and not very false, etc.
Abstract: The term fuzzy logic is used in this paper to describe an imprecise logical system, FL, in which the truth-values are fuzzy subsets of the unit interval with linguistic labels such as true, false, not true, very true, quite true, not very true and not very false, etc. The truth-value set, ℐ, of FL is assumed to be generated by a context-free grammar, with a semantic rule providing a means of computing the meaning of each linguistic truth-value in ℐ as a fuzzy subset of [0, 1]. Since ℐ is not closed under the operations of negation, conjunction, disjunction and implication, the result of an operation on truth-values in ℐ requires, in general, a linguistic approximation by a truth-value in ℐ. As a consequence, the truth tables and the rules of inference in fuzzy logic are (i) inexact and (ii) dependent on the meaning associated with the primary truth-value true as well as the modifiers very, quite, more or less, etc. Approximate reasoning is viewed as a process of approximate solution of a system of relational assignment equations. This process is formulated as a compositional rule of inference which subsumes modus ponens as a special case. A characteristic feature of approximate reasoning is the fuzziness and nonuniqueness of consequents of fuzzy premisses. Simple examples of approximate reasoning are: (a) Most men are vain; Socrates is a man; therefore, it is very likely that Socrates is vain. (b) x is small; x and y are approximately equal; therefore y is more or less small, where italicized words are labels of fuzzy sets.

1,273 citations


"Blood Transfusion System Using Data..." refers background in this paper

  • ...Linguistic principles center around the solution of the problem, not the analysis of the problem [5][6]....

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  • ...This technique centers around what the framework ought to do instead of endeavoring to show how it functions [5]....

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Journal Article
TL;DR: An in-depth analysis of a random forests model suggested by Breiman (2004), which is very close to the original algorithm, and shows in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
Abstract: Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this paper, we offer an in-depth analysis of a random forests model suggested by Breiman (2004), which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.

950 citations


"Blood Transfusion System Using Data..." refers background or methods in this paper

  • ...Another method which can be used for random selection is random split selection where at each node a split is selected randomly from a number of best splits [11]....

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  • ...For analysing the accuracy of Random Forests, certain approaches are there in practice [11]....

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  • ...Such trees are put together to for regression approximate [11]....

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Journal ArticleDOI
TL;DR: Perioperative red blood cell transfusion is the single factor most reliably associated with increased risk of postoperative morbid events after isolated coronary artery bypass grafting.
Abstract: Objective:Our objective was to quantify incremental risk associated with transfusion of packed red blood cells and other blood components on morbidity after coronary artery bypass graftingDesign:The study design was an observational cohort studySetting:This investigation took place at a large tert

858 citations


"Blood Transfusion System Using Data..." refers background or methods in this paper

  • ...ANOVA checked for differences among continuous variables [18]....

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  • ...So based on their calculated logistic regression value we choose whether the patient is prepared for blood transfusion or not [10] [18]....

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  • ...For this investigation, logistic regression within backward, stepwise expulsion of indicator variables are allowed for the prediction of the indicators of the relative rate of being diseased in the population or the stage of being subject to death[18]....

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Posted Content
TL;DR: In this paper, an in-depth analysis of a random forests model suggested by Breiman in the early 2000's is presented, showing that the procedure is consistent and adapts to sparsity, and that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
Abstract: Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this paper, we offer an in-depth analysis of a random forests model suggested by Breiman in \cite{Bre04}, which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.

667 citations