Induction of Decision Trees based on Gray Wolf Optimizer for heart Disease Classification
Pravin S. Game1, Vinod Vaze2, Emmanuel M3
1Pravin S. Game, Research Scholar, JJT University, Jhunjhunu, Rajasthan, India.
2Dr. Vinod Vaze, JJT University, Jhunjhunu, Rajasthan, India.
3Dr. Emmanuel M., Pune Institute of Computer Technology, Pune, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 167-173 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0035028419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the tremendous growth of the data and its usability to help in decision making, it has earned the status of asset today. This applies to medical data too. The decision trees is one of the most sought after tool in data mining for analyzing and representing the data for better visualization and to improve decision making. The deaths due to heart disease are at rise and hence the disease needs special attention not only in identifying that the person is suffering through a heart disease but also to identify which class of heart disease. This paper presents the use of gray wolf optimization to construct decision trees for classification of heart-disease data. The approach explored the opportunity to optimize the decision tree using the nature inspired gray wolf optimizer algorithm. Here in this work, decision trees are used to predict the heart disease. Standard heart disease dataset is used to validate the results. The experimental results are compared with the results from standard decision trees algorithms available in the data mining tool.
Keywords: Decision Trees, Gray wolf Optimizer, Heart Disease, Induction of Trees, Optimization.
Scope of the Article: Classification