Journal
DESIGN AUTOMATION FOR EMBEDDED SYSTEMS
Volume 22, Issue 3, Pages 225-242Publisher
SPRINGER
DOI: 10.1007/s10617-018-9205-4
Keywords
Decision tree; Gain ratio; Gini index; Classification methods; Neural classifier
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available