Grid-Based Interactive Diabetes System

Al-Hazemi, Fawaz; Youn, Chan Hyun; Al‐Rubeaan, Khalid

doi:10.1109/hisb.2011.28

Cited by 4 publications

(7 citation statements)

References 15 publications

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“…Clustering is an unsupervised learning process in which similar objects are grouped into a cluster, without having any predefined classes as compared to classification [34]. There are abundant clustering techniques for diabetes prediction in the literature, such as [99,100,[103][104][105][106][107][112][113][114][115][116][117]. In [102], clustering is used to identify the characteristics that could determine the stage of diabetes and the presence of diabetes in a number of women based on the diabetic and normal classified data.…”

Section: Clustering-based Techniquesmentioning

confidence: 99%

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

et al. 2021

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Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of people around the globe. Its diagnosis, prediction, proper cure, and management are crucial. Data mining based forecasting techniques for data analysis of diabetes can help in the early detection and prediction of the disease and the related critical events such as hypo/hyperglycemia. Numerous techniques have been developed in this domain for diabetes detection, prediction, and classification. In this paper, we present a comprehensive review of the state-of-the-art in the area of diabetes diagnosis and prediction using data mining. The aim of this paper is twofold; firstly, we explore and investigate the data mining based diagnosis and prediction solutions in the field of glycemic control for diabetes. Secondly, in the light of this investigation, we provide a comprehensive classification and comparison of the techniques that have been frequently used for diagnosis and prediction of diabetes based on important key metrics. Moreover, we highlight the challenges and future research directions in this area that can be considered in order to develop optimized solutions for diabetes detection and prediction.

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Section: Clustering-based Techniquesmentioning

confidence: 99%

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

et al. 2021

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“…The workflow is supported by backend Grid-based Interactive Diabetes System (GIDS) [23,24]. The system provides a problem solving environment (computational environment) to study diabetes patient (and could be extended to any chronic disease patients).…”

Section: B Paper Contributionsmentioning

confidence: 99%

“…Our previous work in [23,24] addressed the illustration of the Grid-based Interactive Diabetes System (GIDS). However, in this section we will provide basic understanding of the GIDS.…”

Section: Grid-based Interactive Diabetes Systemmentioning

confidence: 99%

“…However, in this section we will provide basic understanding of the GIDS. GIDS is a web-enabled Grid-based application that uses the distributed computing technology to perform bioinformatics analysis application for diabetes disease [23]. GIDS help medical doctors and scientists who are interested in finding the relationship between diabetes condition of a patient and his/her lifestyle.…”

Section: Grid-based Interactive Diabetes Systemmentioning

confidence: 99%

“…Therefore, based on Legendre, P. et al chronological clustering algorithm [25][26][27], GIDS helps to display the interesting changes into an understandable chart graphs e.g. blood sugar that easily helped in the study [23].…”

Section: Grid-based Interactive Diabetes Systemmentioning

confidence: 99%

See 2 more Smart Citations

GIDS for Chronic Disease Visualization: Implementation of Chronological Clustering as Workflow System

Al-Hazemi¹

2014

LSMR

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Abstract-Chronic disease is linked to patient's' lifestyle. Therefore, doctor has to monitor his/her patient over time. This may involve reviewing many reports taken over short and long periods. Computer applications made it possible to visualize these reports on single displayer such as a timeline-based visualization tool. However, there is a limitation of studying the diabetes patient's history to find out what was the cause of the current development in patient's condition. In this paper, we propose a workflow system which uses the Grid-based Interactive Diabetes System (GIDS) to support diabetes analysis. Technically, the workflow uses an agglomerative clustering algorithm as clustering correlation algorithm which reduces the revision of long reports and focus on vital incidences. However, if doctor demands to review the full reports, the workflow displays the original reports. Through basic evaluation experiment, we were able to demonstrate the usability of the system as chronic patient's multi visualizer.

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A Novel Cluster Algorithms of Analysis and Predict for Brain Derived Neurotrophic Factor (BDNF) Using Diabetes Patients

Devarapalli¹,

Panigrahi

2017

Advances in Intelligent Systems and Computing

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Grid-Based Interactive Diabetes System

Cited by 4 publications

References 15 publications

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

GIDS for Chronic Disease Visualization: Implementation of Chronological Clustering as Workflow System

A Novel Cluster Algorithms of Analysis and Predict for Brain Derived Neurotrophic Factor (BDNF) Using Diabetes Patients

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