A Novel Method for Identification of Cardio Vascular Disease using KELM Optimized by Grey Wolf Algorithm
S. Sharmila1, M.P. Indra Gandhi2

1S. Sharmila: Research Scholar, Department of Computer Science, Mother Teresa Womens University, Kodaikanal, (Tamil Nadu), India.
2M.P. Indra Gandhi: Assistant Professor, Department of Computer Science, Mother Teresa Womens University, Kodaikanal, (Tamil Nadu), India.

Manuscript received on 01 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 July 2019 | PP: 3259-3263 | Volume-8 Issue-9, July 2019 | Retrieval Number: I9006078919/19©BEIESP | DOI: 10.35940/ijitee.I9006.078919

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Timely discovery of the presence of cardiovascular disease can be the difference between life and death. There has been great importance in the construction of processing tools for prognosis and diagnosis of cardiac disease and, especially, cardio vascular events. Classifying data is a customary duty of machine learning. Data mining in health care is a forthcoming arena that attains huge importance for delivering prognosis and a profound realization of medical data. The usage of SVM dependent methodologies in identification the cardio vascular diseases has some restrictions. The important drawbacks of SVM is the severe absence of transparency of outcomes. The ELM learning algorithm is a simple process and it provides accurate result when compared to other traditional algorithms. As the proposal of this research, to enhance the generalization capacity of ELM, KELM is utilized. To improve the classification accuracy of KELM, in this paper a nature inspired swarm intelligence Grey Wolf Algorithm is utilized. Grey Wolf Algorithm is utilized in optimizing the parameter of KELM. By performing classification, accuracy is improved along with high precision and low error rate. Experimental results clearly indicate that the proposed GWO – KELM classifier performs better on comparison with some classifiers that are currently used for the identification of the Cardio Vascular Disease.
Keywords: Cardio Vascular Disease, Machine Learning, Data Mining, Kernel Extreme Learning Machine, Grey Wolf Optimizer.

Scope of the Article: Machine Learning