Optimizing and Enhancing Performance Classification Algorithm on Heart Disease through Feature Selection

Vikas Mongia¹

¹ Dept. of Computer Science, Guru Nanak College, Moga, Panjab- India.

Section:Research Paper, Product Type: Journal
Vol.9 , Issue.6 , pp.1-4, Dec-2021

Online published on Dec 31, 2021

Copyright © Vikas Mongia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The ever-increasing size of datasets in the Big Data era requires effective methods for extracting meaningful information. Data Mining provides a means to analyze large datasets and uncover valuable patterns that can inform future decisions. In this study, we analyze a healthcare dataset of heart diseases to predict the likelihood of a patient having a heart disease based on specific parameters. To accomplish this, we implement decision tree classification algorithms such as ADTree, J48, and RandomForest. Additionally, a feature selection algorithm is applied to remove the least significant three attributes from the dataset, resulting in improved classification performance. Comparing the previous and current results reveals the effectiveness of this approach in enhancing the classification accuracy.

Key-Words / Index Term :
Data Mining, classification algorithms, Feature selection

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