Cardiovascular Disease Prediction Using Data Mining Techniques: A Proposed Framework Using Big Data Approach

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Author(s): Kalyani Kadam (Symbiosis International University (Deemed), India), Pooja Vinayak Kamat (Symbiosis International University (Deemed), India)and Amita P. Malav (Symbiosis International University (Deemed), India)
Copyright: 2019
Pages: 19
Source title: Coronary and Cardiothoracic Critical Care: Breakthroughs in Research and Practice
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8185-7.ch013

Keywords: Cardiovascular Medicine / Medical Diagnosis and Practice / Medical Information Science Reference / Medicine & Healthcare

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Abstract

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

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Cardiovascular Disease Prediction Using Data Mining Techniques: A Proposed Framework Using Big Data Approach

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