Effective Heart Disease Prediction Model for Feature Extraction Model using Data Mining Classification Algorithm
Mobility, load balancing, MCC, cyber foraging
Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases and data warehouses. The most basic definition of data mining is the analysis of large data sets to discover patterns and use those patterns to forecast or predict the likelihood of future events. Data Mining is one of the most critical aspects of automated disease diagnosis and disease prediction. It involves developing data mining algorithms and techniques to analyze medical data. In present, heart disease has excessively increased and heart diseases are becoming one of the most fatal diseases in several countries. In this paper, heart patient datasets are investigate for building classification models in order to predict heart diagnosis. This paper implements feature model construction and comparative analysis for improving prediction accuracy of heart disease in three phases. In first phase, normalization algorithms are applied on the heart patient datasets collected from UCI repository. In second phase, by the use of HCR-PSO (Highly Co-Related Attribute Practical Swarm Optimization) feature selection, using a subset (data) of heart patient from whole normalized heart patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes. Third phase, classification algorithm KNN, Random forest, J48, SVM, Bayesian network and MLP algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification analytical Model before applying HCR-PSO feature selection. But, J48 algorithm is considered as the better performance algorithm after applying HCR-PSO feature selection. In third phase, the results of classification algorithms with and MAE and RMSE validation metric are compared with each other. The results obtained from our experiments indicate that J48 algorithm outperformed all other techniques with the help of feature selection with an accuracy of 94.40%.
Article Details
Unique Paper ID: 148138

Publication Volume & Issue: Volume 5, Issue 12

Page(s): 554 - 563
Article Preview & Download

Share This Article

Go To Issue

Call For Paper

Volume 7 Issue 9

Last Date 25 February 2020

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Email: editor@ijirt.org
Website: ijirt.org