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Ecare@Home: A Distributed Research Environment on Semantic Interoperability Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Classification Model for Predicting Heart Failure in Cardiac Patients

Authors : Muhammad Saqlain, Rao Muzamal Liaqat, Nazar A. Saqib, Mazhar Hameed

Published in: Internet of Things Technologies for HealthCare

Publisher: Springer International Publishing

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Abstract

Today the most significant public health problem is Heart Failure (HF). There are a lot of raw medical data available to healthcare organizations in the form of structured and unstructured datasets, but the need is to analyze this data to get information and to make intelligent decisions. By using data mining, classification tool on a real dataset of cardiac patients we propose a model which classified these patients into four major classes. This model will help to identify the risk of HF and patients who have no HF signs but structural irregularities. We can also identify the patients having HF signs and irregularities and those having the critical stage of HF. This paper provides a detailed summary of modern strategies for management and analysis of HF patients by classes (1 to 4) that have appeared in the past few years.

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previous chapter Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
next chapter Ins and Outs of Big Data: A Review
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Metadata
Title
A Classification Model for Predicting Heart Failure in Cardiac Patients
Authors
Muhammad Saqlain
Rao Muzamal Liaqat
Nazar A. Saqib
Mazhar Hameed
Copyright Year
2016
Book
Internet of Things Technologies for HealthCare

Print ISBN: 978-3-319-51233-4

Electronic ISBN: 978-3-319-51234-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-51234-1_6

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