Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining

Shin, A Mi; Lee, In Hee; Lee, Gyeong Ho; Park, Hee Joon; Park, Hyung Seop; Yoon, Kyung Il; Lee, Jung Jeung; Kim, Yoon Nyun

doi:10.4258/hir.2010.16.2.77

Cited by 38 publications

(26 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This section compares the proposed approach with the state-of-the-art Pattern mining from medical data. Data mining algorithms have largely been exploited to discover interesting patterns among medical data, such as frequent and interesting patterns among patient treatments (e.g., [20,40]), temporal relationships in temporal clinical data [11,18,50], groups of correlated patients [4,5,48], patterns relevant for patient classification (e.g., [26,33,38]). Among the aforementioned approaches, association rules are worth considering in the analysis of healthcare data to transform huge amounts of raw data into actionable knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Three association rule extraction algorithms (i.e., Apriori [3], Predictive Apriori [39], and Tertius [20]) have been investigated. In [40], instead, association rules have been exploited to determine two important diseases in patients diagnosed with essential hypertension, i.e., non-insulin dependent diabetes mellitus and cerebral infarction.…”

Section: Related Workmentioning

confidence: 99%

“…Data mining techniques focus on studying algorithms to find implicit, previously unknown, and potentially useful information from data. In the context of medical care existing data mining approaches encompass different analyses, such as mining underlying associations among data items (e.g., [6,20,34,38,40]), clustering (e.g., [4,5,48]), and classification (e.g., [26,33]). However, the increasing complexity and heterogeneity of medical data prompts the need for novel and effective approaches to automatically mining actionable knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digging deep into weighted patient data through multiple-level patterns

Baralis

Cagliero

Cerquitelli

et al. 2015

Information Sciences

View full text Add to dashboard Cite

Large data volumes have been collected by healthcare organizations at an unprecedented rate. Today both physicians and healthcare system managers are very interested in extracting value from such data. Nevertheless, the increasing data complexity and heterogeneity prompts the need for new efficient and effective data mining approaches to analyzing large patient datasets. Generalized association rule mining algorithms can be exploited to automatically extract hidden multiple-level associations among patient data items (e.g.,

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digging deep into weighted patient data through multiple-level patterns

Baralis

Cagliero

Cerquitelli

et al. 2015

Information Sciences

View full text Add to dashboard Cite

show abstract

“…Unlike the if-then rules of logic, association rules are intrinsically probabilistic and are computed from the data. The ARM is a powerful exploratory technique with a wide range of applications including marketing policies, medical domain (Ilayaraja & Meyyappan 2013;Shin et al 2010), financial forecast, credit fraud detection (Sarno et al 2015) and many other areas. There are a number of famous association rule mining algorithms that are accessible to researchers (Agrawal et al 1993;Burdick et al 2001;Scheffer 2001a).…”

Section: Introductionmentioning

confidence: 99%

Improving rule based classification using harmony search

Hasanpour

Meibodi

Navi

2019

Preprint

View full text Add to dashboard Cite

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

show abstract

“…Unlike the if-then rules of logic, association rules are intrinsically probabilistic and are computed from the data. The ARM is a powerful exploratory technique with a wide range of applications including marketing policies, medical domain (Ilayaraja & Meyyappan, 2013;Shin et al, 2010), financial forecast, credit fraud detection (Sarno et al, 2015) and many other areas. There are a number of famous association rule mining algorithms that are accessible to researchers (Agrawal, Imieliński & Swami, 1993;Burdick, Calimlim & Gehrke, 2001;Scheffer, 2001a).…”

Section: Introductionmentioning

confidence: 99%

Improving rule-based classification using Harmony Search

Hasanpour

Meibodi

Navi

2019

PeerJ Computer Science

View full text Add to dashboard Cite

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

show abstract

Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining

Cited by 38 publications

References 6 publications

Digging deep into weighted patient data through multiple-level patterns

Digging deep into weighted patient data through multiple-level patterns

Improving rule based classification using harmony search

Improving rule-based classification using Harmony Search

Contact Info

Product

Resources

About