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    Prediction of patient's health risk in critical care using a deep neural network

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    msc_swetha_j_2020.pdf (1.870Mb)
    Author
    Swetha, Jamadaguntla
    Date
    2020
    Degree
    MSc in Data Analytics
    URI
    https://esource.dbs.ie/handle/10788/4220
    Publisher
    Dublin Business School
    Rights holder
    http://esource.dbs.ie/copyright
    Rights
    Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
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    Abstract
    The Intensive care units (ICU’s) of a hospital comprise a large share of the health care budget as today’s lifestyle habits and environmental conditions contribute to the onset of chronic diseases. High risk patients in ICU require extensive monitoring and direct attention from healthcare providers. Improving the quality, efficiency, and effectiveness of healthcare provision is an issue of huge importance. Recent technological advances in machine learning have resulted in innovative solutions for the healthcare industry. This research evaluates state of the art deep learning against traditional machine learning algorithms for predicting patients most at risk in critical care. The proposed deep neural network classifier outperforms the traditional methods such as Logistic Regression, GLM, Naïve Bayes, Random Forest and Decision Tree in terms of accuracy, precision, recall, specificity, AUC, and training time. The best performing state of the art deep learning model has an accuracy of 97%.
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