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ThinkMind // GLOBAL HEALTH 2018, The Seventh International Conference on Global Health Challenges // View article global_health_2018_1_10_70037


Open-source Data Analysis and Machine Learning for Asthma Hospitalisation Rates

Authors:
Laura Rooney
Chaloner Chute
William Buchanan
Adrian Smales
Laura Hepburn

Keywords: asthma, copd, machine learning, open source

Abstract:
Long-term conditions in Scotland account for 80% of all GP consultations; they also account for 60% of all deaths in Scotland. Asthma and Chronic Obstructive Pulmonary Disease (COPD) are common long-term respiratory diseases cite{Rutherford2013a}. Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. It is defined by the history of respiratory symptoms such as wheeze, shortness of breath, chest tightness and cough that vary over time and in intensity, together with variable expiratory airflow limitation. So far, we know that there are many different things – such as viruses, allergens, and pollution – that cause asthma or trigger attacks but not why or how they do it. This paper outlines how an open source dataset can be used to estimate asthma hospitalisation rates and uses machine learning to predict these rates, within 7.5%, and for an 86.67% success rate.

Pages: 1 to 7

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4553

ISBN: 978-1-61208-682-8

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018

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