Heart Disease Prediction Model based Ongradient Boosting Tree (GBT) Classification Algorithm
R. Bhuvaneeswari1, P. Sudhakar2, G. Prabakaran3
1R. Bhuvaneeswari, Research Scholar, Department of Computer Science and Engineering Technology, Annamalai University, Chidambaram (Tamil Nadu), India.
2P. Sudhakar, Assistant Professor, Department of Computer Science and Engineering Technology, Annamalai University, Chidambaram (Tamil Nadu), India.
3G. Prabakaran, Assistant Professor, Department of Computer Science and Engineering Technology, Annamalai University, Chidambaram (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 41-51 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10080982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1008.0982S1119
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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: Recently, Heart disease (HD) is the main cause of increasing death rate all over the world. Data classification is a crucial task in the medical field which assists the physicians to predict the diseases. Recently, machine learning (ML) algorithms have been employed to classify the data in the medical field. The data complexity and quantity needs to be examined and managed to transform the efficient and accurate HD diagnosis. In this paper, a gradient boosting tree (GBT) based classifier or gradient boosting classifier (GBC) model to predict the HD efficiently. Besides, a set of extensive experiments were carried out using Staglog and Cleveland heart disease dataset. The experimental values ensured the superiority of the GBT classifier based on several performance measures.
Keywords: Heart Disease, Machine Learning, Classification, Gradient Boosting Tree.
Scope of the Article: Classification