Technological Perspective on Precision Medicine in the Context of Big Data—A Review

Mallika, C.; Selvamuthukumaran, S.

doi:10.1007/978-981-19-2350-0_54

Cited by 2 publications

(1 citation statement)

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“…In this study, no taxonomy was organized. Mallika and Selvamuthukumaran (Mallika and Selvamuthukumaran 2022) provided a review of prospective precision medicine by utilizing big data. They illustrated the most frequently used tools and computational platforms on which precision medicine based its foundation on the role of big data; however, their review had no taxonomy nor any article collection process.…”

Section: Related Workmentioning

confidence: 99%

Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

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Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

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Section: Related Workmentioning

confidence: 99%