The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Intelligent heart disease prediction system using data mining techniques
Abstract
The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
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Heart Disease Prediction using Hybrid machine Learning Model
Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. R., & Suraj, R. S. (2021) · 2021 6th International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358597 · Cited by 429
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Metadata
- Authors
- Palaniappan, S., & Awang, R.
- Venue
- 2008 IEEE/ACS International Conference on Computer Systems and Applications
- Publisher
- IEEE
- Published
- 2008-03
- Pages
- 108-115
- OpenAlex
- W2103069675
- Cited by
- 816
- References
- 10
- FWCI
- 19.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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