A Qualitative Assessment of Machine Learning Support for Detecting Data Completeness and Accuracy Issues to Improve Data Analytics in Big Data for the Healthcare Industry

Juddoo, Suraj; George, Carlisle

doi:10.1109/elecom49001.2020.9297009

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…China Dhiman [67] The use of anonymity technology and differential privacy in data collecting can help avoid attacks based on background information derived through data integration and fusion. India Juddoo & George [68] Examine the prospects for employing machine learning in the process of identifying data incompleteness and inaccuracy, since these two data quality dimensions were considered to be the most significant by the authors' prior research study. Mauritius Roski [69] Investigates these issues as well as the prospects for integrating big data into the healthcare system.…”

Section: Authorsmentioning

confidence: 99%

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

Orlu,

Abdullah,

Zaremohzzabieh

et al. 2023

Sustainability

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The significance of big data analytics (BDA) has benefited the health sector by leveraging the potential insights and capabilities of big data in decision making. However, every implementation of BDA within the healthcare field faces difficulties due to incomplete or flawed information that necessitates attention and resolution. The purpose of this systematic literature review is to accomplish two main objectives. Firstly, it aims to synthesize the various elements that contribute to imperfect information in BDA and their impact on decision-making processes within the healthcare sector. This involves identifying and analyzing the factors that can result in imperfect information in BDA applications. Secondly, the review intends to create a taxonomy specifically focused on imperfect information within the context of BDA in the health sector. The study conducted a systematic review of the literature, specifically focusing on studies written in English and published up until February 2023. We also screened and retrieved the titles, abstracts, and potentially relevant studies to determine if they met the criteria for inclusion. As a result, they obtained a total of 58 primary studies. The findings displayed that the presence of uncertainty, imprecision, vagueness, incompleteness, and complexity factors in BDA significantly impacts the ability to sustain effective decision-making in the healthcare sector. Additionally, the study highlighted that the taxonomy for imperfect information in BDA provides healthcare managers with the means to utilize suitable strategies essential for successful implementation when dealing with incomplete information in big data. These findings have practical implications for BDA service providers, as they can leverage the findings to attract and promote the adoption of BDA within the healthcare sector.

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Section: Authorsmentioning

confidence: 99%

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

Orlu,

Abdullah,

Zaremohzzabieh

et al. 2023

Sustainability

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show abstract

“…In 2020, Juddoo and George [23] addressed how machine learning can be a powerful method for enhancing data quality, particularly in the context of big data, by identifying poor quality data related to insufficiency and inaccuracy. They collected data on EHR Products Used for Meaningful Use Attestation and used RapidMiner Studio to link the dataset as a local data repository.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving medical diagnostics with machine learning: a study on data classification algorithms

2022

IJACR

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“…Spatio-temporal heterogeneity data (STD) [1][2][3] is the data basis for applying big data analysis technology to solve decision-making problems in urban operation and maintenance [4], oil and gas development [5], medical decision-making [6], life sciences [7] and other fields, and its accuracy detection is undoubtedly important [8,9]. Due to the stochasticity and complexity of STD in the temporal dimension and the global and local correlation in the spatial dimension, which makes detection extremely difficult, related research has also become a research hotspot for data analysts [10].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Liu¹,

Zhang²,

Yin³

et al. 2023

Computer Systems Science and Engineering

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Spatio-temporal heterogeneous data is the database for decisionmaking in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology. Secondly, design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy. Finally, the proposed method is compared with three methods, ARIMA, T-GCN, and STGCN, in real scenarios to verify its effectiveness in terms of detection speed, detection accuracy and stability. The experimental results show that the RMSE, MAE, and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree, which are 13.82/12.08, 2.77/2.41, and 16.70/14.73, respectively. Also, it detects the shortest time of 672.31/887.36, respectively. In addition, the evaluation results are the same under different time periods of processing and complex topology environment, which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

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A Qualitative Assessment of Machine Learning Support for Detecting Data Completeness and Accuracy Issues to Improve Data Analytics in Big Data for the Healthcare Industry

Cited by 10 publications

References 17 publications

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era

Improving medical diagnostics with machine learning: a study on data classification algorithms

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

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