Extracting Association Rules from Medical Health Records Using Multi-Criteria Decision Analysis

Lakshmi, K. S.; Vadivu, G.

doi:10.1016/j.procs.2017.09.137

Cited by 42 publications

(21 citation statements)

References 6 publications

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“…To evaluate rule, lift ratio can be used [35,36,[41][42][43][44][45][46][47][48]. Lift ratio is the ratio between the support value of the rule with the antecedent and consequent support value.…”

Section: Association Rulementioning

confidence: 99%

Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

Hikmawati

Maulidevi

Surendro

2021

Preprint

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The process of extracting data to obtain useful information is known as data mining. Furthermore, one of the promising and widely used techniques for this extraction process is association rule mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. The first step in association rule mining is the determination of the frequent item set that will be involved in the rule formation process. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, the minimum support value is determined by the user. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. In this study, a method was proposed to determine the minimum support value based on the characteristics of the dataset. Furthermore, this required certain criteria to be used as thresholds which led to more adaptive rules according to the needs of the user. The results of this study showed that 6 from 8 datasets, obtained a rule with lift ratio > 1 using the minimum threshold value that was determined through this method.

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Section: Association Rulementioning

confidence: 99%

Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

Hikmawati

Maulidevi

Surendro

2021

Preprint

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“…Between the top ten diseases (antecedent) and the related medicine (consequent) was the minimum limit value of support 20% and confidence 65%. Lakshmi and Vadivu (2017) extracted association rules from medical health records using multi-criteria decision analysis. They used a lift value to select interesting rules for the next clinical validation.…”

Section: Related Workmentioning

confidence: 99%

Mild Cognitive Impairment Detection Using Association Rules Mining

Babič¹,

Pusztová²,

Majnarić³

2020

AIP

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A single Mild cognitive impairment (MCI) is a transitional state between normal cognition and dementia. The typical diagnostic procedure relies on neuropsychological testing, which is insufficiently accurate and does not provide information on patients' clinical profiles. The objective of this paper is to improve the recognition of elderly primary care patients with MCI by using an approach typically applied in the market basket analysisassociation rules mining. In our case, the association rules represent various combinations of the clinical features or patterns associated with MCI. The analytical process was performed in line with the CRISP-DM, the methodology for data mining projects widely used in various research or industry domains. In the data preparation phase, we applied several approaches to improve the data quality like the k-Nearest Neighbour, correlation analysis, Chi Merge and K-Means algorithms. The analytical solution´s success was confirmed not only by the novelty and correctness of new knowledge, but also by the form of visualization that is easily understandable for domain experts. This iterative approach provides a set of rules (patterns) that meet minimum support and reliability. The extracted rules may help medical professionals recognize clinical patterns; however, the final decision depends on the expert. A medical expert has a crucial role in this process by enabling the link between the information contained in the rules and the evidence-based knowledge. It markedly contributes to the interpretability of the results.

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“…To improve the service ability of the library, Zhang [6] used Apriori algorithm in library personalized service field. Lakshmi and Vadivu [7] suggested a novel approach to pick up association rules from medical records by choosing the best association rule mining algorithm using multiple-criteria decision analysis.…”

Section: Related Workmentioning

confidence: 99%

Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics

Wang¹

2018

AJAM

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In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.

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Extracting Association Rules from Medical Health Records Using Multi-Criteria Decision Analysis

Cited by 42 publications

References 6 publications

Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

Mild Cognitive Impairment Detection Using Association Rules Mining

Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics

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