Name
Title | ||
|---|---|---|
A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. |
Abstract | ||
|---|---|---|
The healthcare domain is basically “data rich”, yet tragically not every one of the information are dug which is required for finding concealed examples and successful basic leadership used to find learning in database and for restorative research, especially in heart malady forecast. This article has examined forecast frameworks for heart disease utilizing more number of info attributes. In this article, we proposed an altered calculation for classification with decision trees which furnishes precise outcomes when contrasted and others calculations. The proposed work is planned to show the data mining method in disease forecast frameworks in medicinal space by utilizing avaricious way to deal with select the best attributes. Our investigation demonstrates that among various prediction models neural networks and Gini index prediction models results with most noteworthy precision for heart attack prediction. A portion of the discretization strategies like voting technique are known to deliver more precise decision trees. To improve execution in coronary illness finding, this research work examines the outcomes in the wake of applying a scope of procedures to various sorts of decision trees and accuracy and sensitivity are attained by the execution of elective decision tree methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10617-018-9205-4 | Design Autom. for Emb. Sys. |
Keywords | Field | DocType |
Decision tree, Gain ratio, Gini index, Classification methods, Neural classifier | Data mining,Decision tree,Voting,Computer science,Illness (finding),Predictive modelling,Information gain ratio,Artificial neural network | Journal |
Volume | Issue | ISSN |
22 | 3 | 0929-5585 |
Citations | PageRank | References |
20 | 0.88 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Mathan | 1 | 20 | 0.88 |
| Priyan Malarvizhi Kumar | 2 | 46 | 2.09 |
| Parthasarathy Panchatcharam | 3 | 20 | 0.88 |
| Gunasekaran Manogaran | 4 | 48 | 6.97 |
| R. Varadharajan | 5 | 20 | 0.88 |