Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681439
Title: Cloud enabled data analytics and visualization framework for health-shock prediction
Author: Mahmud, S.
ISNI:       0000 0004 5920 3793
Awarding Body: Coventry University
Current Institution: Coventry University
Date of Award: 2016
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Abstract:
Health-shock can be defined as a health event that causes severe hardship to the household because of the financial burden for healthcare payments and the income loss due to inability to work. It is one of the most prevalent shocks faced by the people of underdeveloped and developing countries. In Pakistan especially, policy makers and healthcare sector face an uphill battle in dealing with health-shock due to the lack of a publicly available dataset and an effective data analytics approach. In order to address this problem, this thesis presents a data analytics and visualization framework for health-shock prediction based on a large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services integrated with Geographical Information Systems (GIS) to facilitate the capture, storage, indexing and visualization of big data for different stakeholders using smart devices. The data was collected through offline questionnaires and an online mobile based system through Begum Memhooda Welfare Trust (BMWT). All data was coded in the online system for the purpose of analysis and visualization. In order to develop a predictive model for health-shock, a user study was conducted to collect a multidimensional dataset from 1000 households in rural and remotely accessible regions of Pakistan, focusing on their health, access to health care facilities and social welfare, as well as economic and environmental factors. The collected data was used to generate a predictive model using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting health-shock. The evaluation of the proposed system in terms of the interpretability and accuracy of the generated data models for classifying health-shock shows promising results. The prediction accuracy of the fuzzy model based on a k-fold crossvalidation of the data samples shows above 89% performance in predicting health-shock based on the given factors. Such a framework will not only help the government and policy makers to manage and mitigate health-shock effectively and timely, but will also provide a low-cost, flexible, scalable, and secure architecture for data analytics and visualization. Future work includes extending this study to form Pakistan’s first publicly available health informatics tool to help government and healthcare professionals to form policies and healthcare reforms. This study has implications at a national and international level to facilitate large-scale health data analytics through cloud computing in order to minimize the resource commitments needed to predict and manage health-shock.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.681439  DOI: Not available
Keywords: Cloud Enabled Data ; Analytics ; Visualization Framework ; Health Shock Prediction
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