Efficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms
Published: 16 October 2021
Asian Journal of Research in Computer Science pp 57-68; https://doi.org/10.9734/ajrcos/2021/v12i230281
Abstract: Data mining techniques are used to extract interesting patterns and discover meaningful knowledge from huge amount of data. There has been increasing in usage of data mining techniques on medical data for determining useful trends and patterns that are used in analysis and decision making. About eighty percent of human deaths occurred in low and middle-income countries due to heart diseases. The healthcare industry generates large amount of heart disease data which are not organized. These data make the prediction process more complicated and voluminous. Data mining provides the techniques for fast and accurate transformation of data into useful information for heart diseases prediction. The main objectives of this research is to predict heart diseases more accurately using Naïve Bayes, J48 Decision Tree, Neural Network, Random Forest classification algorithms and compare the performance of classifiers. The research uses raw dataset for performance analysis and the analysis is based on Weka Tool. This research also shows best technique from them which is Random Forest on the basis of accuracy and execution time.
Keywords: decision making / trends and patterns / heart diseases prediction / classification algorithms / Neural / mining
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Click here to see the statistics on "Asian Journal of Research in Computer Science" .