ArticleJournal of medical systems2011
Significant cancer prevention factor extraction: an association rule discovery approach.
Article in Journal of medical systems, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Low Trend for VZV-Associated Disease Patients to Visit Neurologists.Journal of multidisciplinary healthcare · 2023Article
- Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining.Journal of personalized medicine · 2021Article
- Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs.Journal of digital imaging · 2021Article
- Improving rule-based classification using Harmony Search.PeerJ. Computer science · 2019Article
- Fluvoxamine treatment response prediction in obsessive-compulsive disorder: association rule mining approach.Neuropsychiatric disease and treatment · 2019Article
- Comorbidity Study of Attention-deficit Hyperactivity Disorder (ADHD) in Children: Applying Association Rule Mining (ARM) to Korean National Health Insurance Data.Iranian journal of public health · 2018Article
- Data mining for the identification of metabolic syndrome status.EXCLI journal · 2018Review
- The application of data mining techniques to oral cancer prognosis.Journal of medical systems · 2015Article
- Predicting Metabolic Syndrome Using the Random Forest Method.TheScientificWorldJournal · 2015Article
- An association rule mining-based framework for understanding lifestyle risk behaviors.PloS one · 2014Article
- A human-computer collaborative approach to identifying common data elements in clinical trial eligibility criteria.Journal of biomedical informatics · 2013Article
- Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus.EXCLI journal · 2013Article
- Quantitative population-health relationship (QPHR) for assessing metabolic syndrome.EXCLI journal · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.
Indexed as
Identifiers
20703554What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.