Mining association rule based on the diseases population for recommendation of medicine need

Harahap, Mawaddah; Husein, Amir Mahmud; Aisyah, Siti; Lubis, Fajar Rezeki Ananda; Wijaya, Bayu Angga

doi:10.1088/1742-6596/1007/1/012017

Cited by 37 publications

(22 citation statements)

References 4 publications

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“…In other words, dichotomous variables are often found together within a group of interest, and among them, highly correlated variables with respect to a particular target are found with statistical parameters [ 27 ]. The a priori algorithm is the most well-known ARM algorithm and quantifies the correlation of the most frequent occurrences of items with high support, confidence, and lift, showing that each occur frequently within a given group [ 28 ]. The support for the rule (A → B) is the probability that the two behaviors occur together.…”

Section: Methodsmentioning

confidence: 99%

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

Lee

Cartmell

2021

JPM

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We aimed to assess which lifestyle risk behaviors have the greatest influence on the risk of cardiovascular disease in cancer survivors and which of these behaviors are most prominently clustered in cancer survivors, using logistic regression and association rule mining (ARM). We analyzed a consecutive series of 897 cancer survivors from the Korean National Health and Nutritional Exam Survey (2012–2016). Cardiovascular disease risks were assessed using the atherosclerotic cardiovascular disease score (ASCVDs). We classified participants as being in a low-risk group if their calculated ASCVDs was less than 10% and as being in a high-risk group if their score was 10% or higher. We used association rule mining to analyze patterns of lifestyle risk behaviors by ASCVDs risk group, based upon public health recommendations described in the Alameda 7 health behaviors (current smoking, heavy drinking, physical inactivity, obesity, breakfast skipping, frequent snacking, and suboptimal sleep duration). Forty-two percent of cancer survivors had a high ASCVD. Current smoking (common odds ratio, 11.19; 95% confidence interval, 3.66–34.20, p < 0.001) and obesity (common odds ratio, 2.67; 95% confidence interval, 1.40–5.08, p < 0.001) were significant predictors of high ASCVD in cancer survivors within a multivariate model. In ARM analysis, current smoking and obesity were identified as important lifestyle risk behaviors in cancer survivors. In addition, various lifestyle risk behaviors co-occurred with smoking in male cancer survivors.

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Section: Methodsmentioning

confidence: 99%

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

Lee

Cartmell

2021

JPM

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“…A novel approach was proposed for disease prediction using weighted association rules in Lakshmi and Vadivu 2019. Association rule mining is used (Harahap et al 2018) to recommend the medical need. Table 1, summarizes the above mentioned existing research works.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of market–basket analysis on healthcare

Rao

Kiran

Poornalatha

2021

Int J Syst Assur Eng Manag

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Data analysis plays a vital role in the present era as it helps us to understand the patterns by exploring it in meaningful ways. Market—basket is one of the main methods used to find frequently occurring items in a transactional database and many researchers use the Apriori algorithm for this purpose. This paper presents the application of Market Basket Analysis to the healthcare section. The present work tries to find frequent diseases that occur together in an area by using the Apriori algorithm. This could help the residents of an area to be more cautious about the frequently occurring diseases and take all possible precautionary measures to safeguard their health. In addition, it could also help the doctors so that, they are ready with required medications to treat the patients.

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“…Patients with metabolic syndrome have a higher connection with liver diseases than patients with diabetes mellitus. In [3] analyzed patient prescriptions to identify the relationship between the disease and medicines that are used to treat the patient's illness. The patient prescription datasets in 2015 and 2016 from two hospitals are collected to find the relationship.…”

Section: Related Workmentioning

confidence: 99%

“…Association rule mining is a technique that has been widely used in the clinical domain. It is applied to find association patterns of diseases, such as finding the relation of metabolic syndrome and other diseases [2], finding the relation of the disease and medicines [3], and finding the relation of factors and disease [4].…”

Section: Introductionmentioning

confidence: 99%

Finding Association Patterns of Disease Co-occurrence by using Closed Association Rule Generation

Songram¹,

Chompowiset²,

Jareanpon³

2020

Adv. sci. technol. eng. syst. j.

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This paper proposes a closed association rule generation technique to investigate the association patterns of diseases that are frequent co-occurrence. Diseases records of 5,000 patients are studied to find the association patterns of disease co-occurrence. The CHARM algorithm is adapted to find frequent diseases that can cover all-important patterns with a small number. Then the association patterns of disease co-occurrence are created in a form of association rules from the frequent diseases. The rules represent diseases associated with other diseases. Accuracy and prediction ratio are defined to evaluate the generated association patterns. From the experimental results, the generated association patterns give 79.76% of accuracy and 84.03% of prediction ratio although the number of generated association patterns is small. Moreover, the top-10 association patterns of disease cooccurrence are investigated. Besides, the 5 most frequent diseases are found to deeply study the other related diseases of them. From the investigation, we found that diabetes mellitus, metabolic disorders, and renal failure are highly related to hypertensive diseases with 88.81% of confidence. In addition, we found that influenza and pneumonia, plastic and other anemias are highly related to metabolic disorders.

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Mining association rule based on the diseases population for recommendation of medicine need

Cited by 37 publications

References 4 publications

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

Application of market–basket analysis on healthcare

Finding Association Patterns of Disease Co-occurrence by using Closed Association Rule Generation

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