|
A state of art heart disease prediction techniques based on evolutionary algorithms
|
|
| Author:
|
R.THANGA SELVI, , I.MUTHULAKSHMI
|
| Abstract:
|
In the past decades, heart disease (HD) is an important reason for the increased mortality rate. It is a demanding
problem to build powerful and reliable medical decision support systems (MDSS) to diminish the diagnosing time and
enhancing the accuracy of diagnosis. Among the massive data, to explore the hidden patterns, data mining provides
various approaches. From the medical data of patient, HD can be diagnosed using the data mining approaches. Various
classification algorithms are built to diagnose the HD correctly. Classification rule mining is a significant job in the data
mining development which is planned to search a tiny collection of rules out of the training data set. This paper
reviews several classification algorithms based on evolutionary algorithms (EA) developed for the prediction of HD in
various aspects. A detailed review is made based on the objectives, methodology used, advantages, performance
measures used and so on. At the end of the paper, a comparative analysis is made based on different metrics.
|
| Keyword:
|
Heart disease, Classification, Machine learning, Disease prediction.
|
| EOI:
|
-
|
| DOI:
|
https://doi.org/10.31838/ijpr/2019.11.01.091
|
| Download:
|
Request For Article
|
|
|