A Systematic Literature Review on Cardiovascular Disorder Identification using Knowledge Mining and Machine Learning Methods
Syed Immamul Ansarullah1, Pradeep Kumar2

1Syed Immamul Ansarullah, Research Scholar, Department of CS & IT, Maulana Azad National Urdu University, Hyderabad (Telangana), India.
2Dr. Pradeep Kumar, Associate Professor, Department of CS & IT, Maulana Azad National Urdu University, Hyderabad (Telangana), India.
Manuscript received on 28 March 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 18 April 2019 | PP: 1009-1015 | Volume-7 Issue-6S March 2019 | Retrieval Number: F04050376S19/2019©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: Cardiovascular diseases are challenging to predict and diagnose due to the underlying dysfunctions associated with reflex mechanisms. Considering the mortality ratio and economy burden by the cardiovascular disorder, various researchers seek to diagnose this pernicious disease at its earliest by analyzing the healthcare data. In recent times, researchers made seminal contributions however, the unavailability of an extensive and fundamental article motivated us to prepare a literature review on a cardiovascular disease. We conduct a comprehensive database search between the years 2000 and 2017 using different keyword combinations to get distinguished articles about the disease. We provide descriptive insights to fill the uncovered research gaps. This paper attempts to uncover the state-of-the-art data mining approaches and tools that can be used to diagnose the cardiovascular disease at its initial. To our knowledge until now there is no competent and comprehensive article on cardiovascular disorder prognosis and identification using knowledge mining and machine learning approaches. The topic is diverse as well progressive hence demands additional research to understand newly identified discoveries about the disease.
Keywords: Data Mining, Association Rules, Cardiovascular Disease, Classification, Clustering, Healthcare Industry.
Scope of the Article: Machine Learning