| 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 274 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 274 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 38 | 14% |
| Student > Master | 37 | 14% |
| Lecturer | 14 | 5% |
| Student > Ph. D. Student | 13 | 5% |
| Researcher | 9 | 3% |
| Other | 29 | 11% |
| Unknown | 134 | 49% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 77 | 28% |
| Engineering | 18 | 7% |
| Mathematics | 9 | 3% |
| Nursing and Health Professions | 6 | 2% |
| Unspecified | 5 | 2% |
| Other | 20 | 7% |
| Unknown | 139 | 51% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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
#8,538,940
of 25,382,440 outputs
Outputs from International Journal of Nanomedicine
#1,077
of 4,122 outputs
Outputs of similar age
#139,446
of 344,853 outputs
Outputs of similar age from International Journal of Nanomedicine
#17
of 81 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,122 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 63% 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 344,853 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 51% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.