Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review

Abstract

OBJECTIVES: This scoping review synthesizes literature on the integration of large-scale data analytics for cardiovascular disease (CVD) prediction, aiming to provide insights that support the adoption of predictive analytics for improved prevention and early detection in healthcare. METHODS: Searches were conducted in Medline (PubMed), EBSCO, Google Scholar, and Wiley Online Library. Medical Subject Headings (MeSH) search terms included: large-scale data, big data, cardiovascular diseases, prediction, machine-learning algorithms, artificial intelligence, and mortality. The search covered the period from 2020 to 2024. RESULTS: Of 262 retrieved articles, 16 were included. Three main themes were identified: large-scale data analysis techniques and machine-learning algorithms; applications of machine-learning algorithms and artificial intelligence in predicting cardiovascular diseases; and the role of integrating large-scale data in disease prediction to improve the quality of care. CONCLUSIONS: While machine learning provides considerable opportunities for predicting CVD outcomes, limitations remain. Machine-learning approaches are not always the most appropriate option, particularly in basic research where causal relationships between variables may be more critical than optimized predictions. To ensure fair and effective healthcare outcomes, issues related to bias, data quality, ethical concerns, and practical implementation must be addressed. Overcoming these challenges will require interdisciplinary collaboration, methodological refinement, and further research.

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