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Effective heart disease prediction system using data mining techniques

Overview of attention for article published in International Journal of Nanomedicine, March 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
4 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
109 Mendeley
Title
Effective heart disease prediction system using data mining techniques
Published in
International Journal of Nanomedicine, March 2018
DOI 10.2147/ijn.s124998
Pubmed ID
Authors

Poornima Singh, Sanjay Singh, Gayatri S Pandi-Jain

Abstract

The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, an effective heart disease prediction system (EHDPS) is developed using neural network for predicting the risk level of heart disease. The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The EHDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge, eg, relationships between medical factors related to heart disease and patterns, to be established. We have employed the multilayer perceptron neural network with backpropagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict the risk level of heart diseases.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 29 27%
Student > Master 26 24%
Researcher 7 6%
Lecturer 6 6%
Student > Ph. D. Student 5 5%
Other 11 10%
Unknown 25 23%
Readers by discipline Count As %
Computer Science 54 50%
Engineering 13 12%
Mathematics 6 6%
Decision Sciences 3 3%
Nursing and Health Professions 3 3%
Other 5 5%
Unknown 25 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 October 2020.
All research outputs
#4,643,704
of 16,713,463 outputs
Outputs from International Journal of Nanomedicine
#387
of 3,051 outputs
Outputs of similar age
#96,067
of 283,612 outputs
Outputs of similar age from International Journal of Nanomedicine
#3
of 46 outputs
Altmetric has tracked 16,713,463 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 3,051 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 283,612 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.