A Divided Latent Class analysis for Big Data

Abarda, Abdallah; Bentaleb, Youssef; Mharzi, Hassan

doi:10.1016/j.procs.2017.06.111

Cited by 9 publications

(3 citation statements)

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“…Big data are increasingly useful to scientists, health practitioners and the society in general (Shu 2016). To improve big data usefulness for healthcare services, it requires analytics (Abarda, Bentaleb & Mharzi 2017). Big data analytics refer to a collection of analytic techniques and technologies that have been specifically designed to analyse big data to inform decision-making (Kwon et al 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

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A framework for selecting analytics tools to improve healthcare big data usefulness in developing countries

Iyamu

2020

SA Journal of Information Management

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Background: In many developing countries including South Africa, there are challenges in understanding how the different networks of patients, diagnoses, and medical personnel are formed, as well as the types of big data that are generated. The challenges include the relationship and interaction that exist between the big data within the various networks. Some of the challenges manifest into different factors such as inaccuracy of data, inconsistency, incompleteness of data, and lack of cohesion. The trajectory of the challenge is the inability to select the most appropriate analytics tools for big data analysis. Objectives:The objective of the study was to propose a solution that can be used to address the challenges in selecting analytics tools, to enhance big data usefulness for the improvement of healthcare services, particularly in developing countries.Method: Literature of within 10 years of publication in the areas of big data, big data analytics and healthcare were gathered and used as data. The analysis of the data followed the hermeneutics approach within the interpretivist paradigm.Results: From the analysis, factors that influence big data and analytics tools' usefulness were found, based on which a solution (framework) is proposed. The solution is intended to contribute to the works of information systems and technologies (IS/IT) personnel, health practitioners and academics. Conclusion:The relationships that exit between actors, and the actors' interactions in the process of providing medical services contribute to the sources of big data. Therefore, it is necessary to holistically analyse healthcare big data from both technical and non-technical perspectives.

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Section: Literature Reviewmentioning

confidence: 99%

“…Increasingly, big data are employed for healthcare services, such as a divided latent class analysis for big data. Abarda, Bentaleb and Mharzi (2017); Ganjir, Sarkar and Kumar (2016); Sun and Reddy (2013).…”

Section: Object Of Focus Description Referencesmentioning

confidence: 99%

A framework for selecting analytics tools to improve healthcare big data usefulness in developing countries

Iyamu

2020

SA Journal of Information Management

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“…For example, latent class clustering uses expectation maximization to estimate model parameters. Scaling expectation maximization and latent class clustering to big datasets is non-trivial (see [6] and [7]). Similarly, dissimilarity based approaches to clustering have to overcome the computational and storage (memory) hurdles associated with computing and storing a large dissimilarity matrix.…”

Section: Introductionmentioning

confidence: 99%

Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

Sambasivan

Das

2018

Calcutta Statistical Association Bulletin

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Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. In this work we examine an approach to clustering such datasets using homogeneity analysis. Homogeneity analysis determines a euclidean representation of the data. This can be analyzed by leveraging the large body of tools and techniques for data with a euclidean representation. Experiments conducted as part of this study suggest that this approach can be useful in the analysis and exploration of big datasets with a mixture of numerical and categorical attributes.

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