Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Darwish, Saad M.; Essa, Reham M.; Osman, Mahmoud M.; Ismail, Ahmed A.

doi:10.1109/access.2022.3192447

Cited by 25 publications

(14 citation statements)

References 58 publications

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“…Patient medical examination data and outpatient medical data are analyzed using our suggested method for determining a correlation between the illness and the test findings. Regarding the results related to extracting negative correlation rules for each database, based on the same configurations applied for GA and TS and the same measures, the same results were obtained as in [66].…”

Section: Resultsmentioning

confidence: 83%

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An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

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It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

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Section: Resultsmentioning

confidence: 83%

“…The approach discussed in [66] for preserving privacy of association rule mining in healthcare databases does not take into consideration how to preserve privacy in the case of distributed data mining. The proposed method protects patient privacy without compromising the efficacy of vertically partitioned healthcare databases.…”

Section: Resultsmentioning

confidence: 99%

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For medical research to be of higher caliber, data from of the EMR system must be shared. These data are used by researchers to carry out a variety of data mining tasks [4], such as classification (predict the occurrence of diabetes), clustering (identify risks), and statistical tests (correlation between body mass index and diabetes), or query replying [5]. Healthcare researchers are anticipated to benefit from the integration of data and electronic medical records, which should also improve the actualized patient care [6].…”

Section: Introductionmentioning

confidence: 99%

A Review on Privacy Preserving Using Machine learning and Deep Learning Techniques

Rajput¹,

Tiwari²

2023

IJRASET

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In order to entice data custodians to provide precise documentation so that data mining can continue with confidence, protecting the confidentiality of healthcare data is crucial. Association rule mining has been extensively used in the past to analyze healthcare data. The majority of applications ignore the drawbacks of specific diagnostic procedures in favor of positive association criteria. Negative association criteria may provide more useful information when bridging disparate diseases and medications than positive ones. In the case of doctors and social groups, this is particularly accurate. Data mining for medical purposes must be done with patient identities protected, especially when working with sensitive data. However, it might be attacked if this information becomes public. In order to perform data mining research, technology that modifies data (data sanitization) that reconstructs aggregate distributions has recently addressed the importance of healthcare data privacy. This study examines data sanitization in healthcare data mining using metaheuristics in order to safeguard patient privacy. Studies on SHM have looked at the uses of IoT &/or Machine Learning (ML) within the field, as well as the architecture, security, & privacy issues. However, no studies have looked into how AI and ubiquity computing technologies have affected SHM systems. The objective of this research is to identify and map the primary technical concepts within the SHM framework.

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“…[1][2] Some experts also designed and implemented a LIS data analysis system based on web data mining and concurrent access control technology [3][4]. In addition, some experts believe that data warehouse and data mining technology can better organize big data, make it play its best role, and provide a basis for business decision-making [5][6]. Therefore, the research on the application of machine learning in data analysis in this paper has theoretical basis and is conducive to the design of data processing and analysis system.…”

Section: Introductionmentioning

confidence: 99%

Practice and Application of Fusion Machine Learning in Data Analysis

2023

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Machine learning is a process in which computer is used to train and calculate input data and output results in a complex, multi task simulation. In data analysis, we can use machine learning to carry out experimental research and theoretical verification. In order to improve the ability of data analysis, we need to use machine learning and data mining methods to better process data. In this paper, experimental method and principal component analysis method are mainly used to test and discuss the fusion of machine learning in data analysis. The experimental results show that the CPU utilization rate in Scheme 4 is about 85% on average. The reason why the CPU of the Scribe center server is reduced is that after receiving data, there is less data to decompress, which reduces the CPU utilization.

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Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Cited by 25 publications

References 58 publications

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

A Review on Privacy Preserving Using Machine learning and Deep Learning Techniques

Practice and Application of Fusion Machine Learning in Data Analysis

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