A systematic review of data mining applications in kidney transplantation

Aslani, Nasim; Galehdar, Nasrin; Garavand, Ali

doi:10.1016/j.imu.2023.101165

Cited by 1 publication

(2 citation statements)

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“…The logical structure obtains the correct synaptic weight in order to achieve the global minimum value. The possible logical structure after the permutation is shown in Equations ( 4) and (5).…”

Section: Higher-order 3-satisfiability Representationmentioning

confidence: 99%

“…In theory, data mining enables us to make an informed decision or to explore the outcome of a decision without making the decision itself. Thus, this method of handling data can be used in a multitude of real-life applications, including those in the medical [5], water research [6], stock market [7], data mining [8], landslide prediction [9], education [10], and diagnostics [11] fields, among others [12,13]. With the rapid advancement of science and technology, it is vital to return to the fundamentals of data mining.…”

Section: Introductionmentioning

confidence: 99%

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Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

et al. 2023

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Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.

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Section: Higher-order 3-satisfiability Representationmentioning

confidence: 99%