Open AccessEfficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms
Author(s) -
Md. Ashikur Rahman Khan,
Masudur Rahman,
Jayed Us Salehin,
Md. Saiful Islam,
Md. Fazle Rabbi
Publication year - 2021
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2021/v12i230281
Subject(s) - c4.5 algorithm , decision tree , computer science , raw data , data mining , naive bayes classifier , random forest , statistical classification , machine learning , process (computing) , artificial intelligence , artificial neural network , support vector machine , programming language , operating system
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.