Cloud enabled data analytics and visualization framework for health-shocks prediction

Abstract

In this paper, we present a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset. The framework is developed using cloud computing services based on Amazon web services (AWS) integrated with geographical information systems (GIS) to facilitate big data capture, storage, index and visualization of data through smart devices for different stakeholders. In order to develop a predictive model for health-shocks, we have collected a unique data from 1000 households, in rural and remotely accessible regions of Pakistan, focusing on factors like health, social, economic, environment and accessibility to healthcare facilities. We have used the collected data to generate a predictive model of health-shock using a fuzzy rule summarization technique, which can provide stakeholders with interpretable linguistic rules to explain the causal factors affecting healthshocks. The evaluation of the proposed system in terms of the interpret-ability 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 cross-validation of the data samples shows above 89% performance in predicting health-shocks based on the given factors.

Big data analytics: Computational intelligence techniques and application areas

“…This can be seen in the recent work by Mahmud et al where a data analytics and visualization framework for health shocks prediction was proposed, utilizing cloud computing services on Amazon, integrated with geographical information systems able to support Big Data requirements and visualization through smart devices. The researchers have also collected data from a large number of households in different regions of Pakistan and have developed a fuzzy predictive model, which comprised of interpretable fuzzy rules which provided stakeholders with a clear view of the factors influencing health shocks (Mahmud et al, 2016). Another framework for cloud based Big Data analytics and visualization was presented in Lu et al (Lu et al, 2011).…”

Geo-Visualisation and Visual Analytics for Smart Cities: A Survey

“…Smart healthcare related analytics tools allow healthcare specialists to collect and analyse patients' data, which can likewise be used by insurance agencies and administration organisations. Moreover, proper analytics of large healthcare data can help predict epidemics, cures, and diseases, as well as improve quality of life and avoid preventable death (Noon and Hankins, 2001, Vanus et al, 2014, Jeong et al, 2016, Mahmud et al, 2016, Marsal-Llacuna and Segal, 2016, Kunjir et al, 2017, Muhammad Babar, 2017, Tunio et al, 2017.…”

Medical Data Visualization Analysis and Processing Based on Machine Learning

“…The distance between two planes is 2 w  , where w  stands for Euclidean norm. Such task situations are expressed as a set of constraints (5). When the data is non-linear and separable, the constraint condition of the task case is (6).…”

“…Seonah Lee [4] developed time-oriented visualization for problems and outcomes and Matrix visualization for problems and interventions by using PHN-generated Omaha System data to help PHNs consume data and plan care at the point of care. In 2016, Shahid Mahmud [5] presented a data analytics and visualization framework for health-shocks prediction based on large-scale health informatics dataset based on fuzzy rule summarization, which can provide interpretable linguistic rules to explain the causal factors affecting health-shocks. Usman Iqbal [6] put forward an animated visualization tool called as Cancer Associations Map Animation (CAMA), which can depict the association of 9 major cancers with other disease over time based on 782 million outpatient data in health insurance database.…”