Wyniki wyszukiwania - BazTech

1

Suspected adverse drug reaction detection using association rules mining and fuzzy sets

Mansour Ayman M., Khazalah Fayez, Obeidat Mohammad A.

Przegląd Elektrotechniczny

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2022

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R. 98, nr 1

34--43

EN

Finding adverse drug reaction ADRs is vital to sustaining human life. The effect of the drug under several factors such as age, drug quantity, laboratory results, sex and drug duration are necessary to improve the quality of human body treatment has not been used previously. The paper uses real databases collected from US hospitals to validate the developed detection system. This paper presents an intelligent system based on association rule mining rules and fuzzy set theory. The developed system has the potential to determine the relationships between a drug and its adverse reactions. This is done by extracting several rules with high support and confidence. Two physicians review the results of the proposed system to validate the results. The results matches the ADRs defined by medical associations and drug companies.

PL

Znalezienie niepożądanych reakcji na leki ADR ma kluczowe znaczenie dla podtrzymania życia ludzkiego. Wpływ leku na kilka czynników, takich jak wiek, ilość leku, wyniki laboratoryjne, płeć i czas trwania leku są niezbędne do poprawy jakości leczenia ludzkiego ciała, nie były wcześniej stosowane. W artykule wykorzystano rzeczywiste bazy danych zebrane ze szpitali w USA do walidacji opracowanego systemu wykrywania. W artykule przedstawiono inteligentny system oparty na regułach eksploracji reguł asocjacyjnych i teorii zbiorów rozmytych. Opracowany system ma potencjał do określenia zależności między lekiem a jego działaniami niepożądanymi. Odbywa się to poprzez wyodrębnienie kilku reguł z dużym wsparciem i pewnością. Dwóch lekarzy dokonuje przeglądu wyników proponowanego systemu, aby je zweryfikować. Wyniki są zgodne z ADR-ami zdefiniowanymi przez stowarzyszenia medyczne i firmy farmaceutyczne.

2

High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce

Bisoyi Sudhanshu Shekhar, Mishra Pragnyaban, Mishra Saroja Nanda

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 2

177--186

EN

Increasing development in information and communication technology leads to the generation of large amount of data from various sources. These collected data from multiple sources grows exponentially and may not be structurally uniform. In general, these are heterogeneous and distributed in multiple databases. Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge. Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS). The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment. In this situation synthesizing highfrequency rules from the big database becomes more challenging. Many algorithms for synthesizing association rule have been proposed in multiple database mining environments. These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules. In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS. Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed. Experiments have been conducted in a parallel and distributed environment by using commodity hardware. We ensure the efficiency, scalability, high availability and costeffectiveness of our proposed method.

3

Correlation analysis and prevention of electrocution risk factors in the construction industry

Li Jue, Chen Feifei, Li Shije

Archives of Civil Engineering

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2022

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Vol. 68, nr 4

537--554

EN

Electrocution is one of the main causes of workplace deaths in the construction industry. This paper presents a framework for identifying electrocution risk factors and exploring the correlations between them, with the aim of assisting accident prevention research. Specifically, the Haddon Matrix is used to extract the risk factors from 193 investigation reports of electrical shock accidents from 2012-2019, and the Apriori algorithm is applied to examine the potential relationships between these factors. Based on association rules using three criteria: support (S), confidence (C) and lift (L), the betweenness centrality is then introduced to optimize association rules and find the most important rules though comparison. The results show that after optimization, some of these critical rules rise significantly in rank, such as Workplace: indoor → No CPR provided. Through these ranking changes, the focus of safety management is clarified, and finally, based on a comprehensive analysis of association rules, targeted accident prevention measures are suggested.

4

Cross-selling models for telecommunication services

Jaroszewicz S.

Journal of Telecommunications and Information Technology

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2008

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nr 3

52-59

EN

Cross-selling is a strategy of selling new products to a customer who has made other purchases earlier. Except for the obvious profit from extra products sold, it also increases the dependence of the customer on the vendor and therefore reduces churn. This is especially important in the area of telecommunications, characterized by high volatility and low customer loyalty. The paper presents two cross-selling approaches: one based on classifiers and another one based on Bayesian networks constructed based on interesting association rules. Effectiveness of the methods is validated on synthetic test data.