Association rule mining to detect factors which contribute to heart disease in males and females

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S.; Chen, Yi‐Ping Phoebe

doi:10.1016/j.eswa.2012.08.028

Cited by 335 publications

(150 citation statements)

References 29 publications

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“…Based on these rules, this research discovered the factors which caused heart problem in men and women. After analyzing the rules authors conclude that women have less possibility of having coronary heart disease as compare to men [118].…”

Section: Association Examples In Healthcarementioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

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Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

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Section: Association Examples In Healthcarementioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

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show abstract

“…It was seen that the proposed approach yielded results in a very short period of time [20]. Nahar et al identified significant risk factors for heart attacks in men and women with the rules of association [21]. Ceylan , in his work in a pharmacy, used patient prescription data to analyze associations between drugs.…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector

Sagin

Ayvaz

2018

scjournal

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“…Nahar et al [10] investigated the sick and healthy factors which contribute to heart disease for males and females. To identify these factors, the author had used an approach called Association rule mining with UCI Cleveland dataset with the three rule generation algorithms like Apriori, Predictive Apriori and Tertius.…”

Section: Related Workmentioning

confidence: 99%

Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

Rathi¹,

Narasimhan²

2017

IJARCSSE

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Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient's life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several mechanisms namely apriori algorithm, frequent itemset mining, support vector machine, neural network, classification and regression trees, fuzzy rule-based clinical decision support system, k-nearest neighbor, genetic algorithm, scoring system, nature language processing (NLP) techniques, type-2 fuzzy logic system, decision tree and statistical methods are used to classify heart disease prediction. From this survey research, it is identified that support vector machine algorithm outperforms all the other methods in terms of classification accuracy.

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Association rule mining to detect factors which contribute to heart disease in males and females

Cited by 335 publications

References 29 publications

Techniques of Data Mining In Healthcare: A Review

Techniques of Data Mining In Healthcare: A Review

Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector

Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

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