Examining strategic diversity communication on social media using supervised machine learning: Development, validation and future research directions

Hofhuis, Joep; Gonçalves, João; Schafraad, Pytrik; Wu, Biyao

doi:10.1016/j.pubrev.2024.102431

Public Relations Review

2024

DOI: 10.1016/j.pubrev.2024.102431

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Examining strategic diversity communication on social media using supervised machine learning: Development, validation and future research directions

Joep Hofhuis,

João Gonçalves,

Pytrik Schafraad

et al.

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2024

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Cited by 2 publications

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Computational strategic communication in a data-driven world

Zhou,

van der Meer

2024

Public Relations Review

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Computational strategic communication in a data-driven world

Zhou,

van der Meer

2024

Public Relations Review

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A comprehensive review for chronic disease prediction using machine learning algorithms

Islam,

Sultana,

Islam

2024

Journal of Electrical Systems and Inf Technol

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The past few years have seen an emergence of interest in examining the significance of machine learning (ML) in the medical field. Diseases, health emergencies, and medical disorders may now be identified with greater accuracy because of technological advancements and advances in ML. It is essential especially to diagnose individuals with chronic diseases (CD) as early as possible. Our study has focused on analyzing ML’s applicability to predict CD, including cardiovascular disease, diabetes, cancer, liver, and neurological disorders. This study offered a high-level summary of the previous research on ML-based approaches for predicting CD and some instances of their applications. To wrap things up, we compared the results obtained by various studies and the methodologies as well as tools employed by the researchers. The factors or parameters that are responsible for improving the accuracy of the predicting model for different previous works are also identified. For identifying significant features, most of the authors employed a variety of strategies, where least absolute shrinkage and selection (LASSO), minimal-redundancy-maximum-relevance (mRMR), and RELIEF are extensively used methods. It is seen that a wide range of ML approaches, including support vector machine (SVM), random forest (RF), decision tree (DT), naïve Bayes (NB), etc., have been widely used. Also, several deep learning techniques and hybrid models are employed to create CD prediction models, resulting in efficient and reliable clinical decision-making models. For the benefit of the whole healthcare system, we have also offered our suggestions for enhancing the prediction results of CD.

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Examining strategic diversity communication on social media using supervised machine learning: Development, validation and future research directions

Cited by 2 publications

References 63 publications

Computational strategic communication in a data-driven world

Computational strategic communication in a data-driven world

A comprehensive review for chronic disease prediction using machine learning algorithms

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