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ThinkMind // eTELEMED 2018, The Tenth International Conference on eHealth, Telemedicine, and Social Medicine // View article etelemed_2018_8_30_40110

Toward Robust Heart Failure Prediction Models Using Big Data Techniques

Authors:
Heba Rammal
Ahmed Emam

Keywords: Big Data; Hadoop; Healthcare; Heart Failure; Prediction Model.

Abstract:
Big Data technologies have a great potential in transforming healthcare, as they have revolutionized other industries. In addition to reducing the cost, they could save millions of lives and improve patient outcomes. Heart Failure (HF) is the leading death cause disease globally. The social and individual burden of this disease can be reduced by its early detection. However, the signs and symptoms of HF in the early stages are not clear, so it is relatively difficult to prevent or predict it. The main objective of this paper is to propose a model to predict patients with HF using a multi-structure dataset integrated from various resources. The underpinning of our proposed model relies on studying the current analytical techniques that support heart failure prediction, and then build a model based on Big Data technologies. To achieve this, we extracted different important factors of heart failure from King Saud Medical City (KSUMC) system, Saudi Arabia, which are available in structured, semi-structured and unstructured format. Unfortunately, a lot of information is buried in unstructured data format. We applied some pre-processing techniques to enhance the parameters and integrate different data sources in Hadoop Distributed File System (HDFS). Then, we applied data-mining algorithms to discover patterns in the dataset to predict heart risks and causes. Finally, the analyzed report is stored and distributed to get the insight needed from the prediction.

Pages: 85 to 91

Copyright: Copyright (c) IARIA, 2018

Publication date: March 25, 2018

Published in: conference

ISSN: 2308-4359

ISBN: 978-1-61208-618-7

Location: Rome, Italy

Dates: from March 25, 2018 to March 29, 2018

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