Hadoop based Analysis and Visualization of Diabetes Data through Tableau

Bhardwaj, Priti; Baliyan, Niyati

doi:10.1109/ic3.2019.8844873

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…They test the usability of diabetes data especially type 1 diabetes (T1D). Bhardwaj et al [7] performed Hadoop-based analysis to visualize diabetes data using Tableau software. They explored diabetes case studies and proposed a comparison among Pig, Hive, and Tableau.…”

Section: Visual Analytics For Explorationmentioning

confidence: 99%

“…However, traditional approaches are limited in their ability to consider such a large amount of data, where advanced technology such as visual analytics [19] provides the insightful impact to predict diabetes disease as well as identify the most significant factors. For example, Bhardwaj and Baliyan [7] visualized diabetics data in different ways and also proposed an interactive visualization system using Tableau. They explored how people are affected by diabetes of all ages.…”

Section: Introductionmentioning

confidence: 99%

“…[22,24,48]. Visualizing diabetes data [44], Hadoop based analysis and visualization of diabetes [7], were emerged to provide a visual overview of diabetic disease analysis. However, their contribution focused on structured data, whereas our implementation provides an interactive and effective dashboard that flourish more insights regarding diabetes disease.…”

Section: Introductionmentioning

confidence: 99%

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DiaVis: Exploration and Analysis of Diabetes through Visual Interactive System

Rahman¹,

Islam²,

Akter³

et al. 2021

HCIS

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Background: Diabetes is a long-term disease characterized by high blood sugar and has risen as a public health problem globally. Exploring and analyzing diabetes data is a timely concern because it may prompt a variety of serious illnesses, including stroke, kidney failure, heart attacks, etc. Several existing pieces of research have revealed that diabetes data, such as systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, height, age, etc., can provide insightful information about patients diabetes states. However, very few studies have focused on visualizing diabetes mellitus (DM) insights to support healthcare administrator (HA)'s goals adequately, such as (i) decision-making, (ii) identifying and grouping associated factors, and (iii) analyzing large data effectively remains unexplored.Objective: This study aims to design an interactive visual system (Vis) to explore diabetes mellitus (DM) insights and its associated factors in Bangladesh. Methods:In this study, first, a case study method has employed to understand diabetes data. Second, we examine the potential of user-centered technology in addressing these challenges and design a Vis named "DiaVis" to process and present raw data in the form of graphics, graphs, and processed text, as well as a variety of user interaction possibilities. It helps to extract valuable data and present it in a simple and easy-to-understand way. Moreover, we highlight some key insights from our study that may help explore the healthcare community.Results: A user study with 20 individuals is used to evaluate our system. By allowing iterative exploration and modification of data in a dashboard with multiple-coordinated views, the DiaVis system improves the flow of visual analysis. Conclusion:This study suggests that the healthcare community should pay more attention to developing appropriate policy measures to reduce the risk of DM.

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Section: Visual Analytics For Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%