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 preventionand early detection in healthcare. Methods: Searches were conducted in Medline (PubMed), EBSCO, Google Scholar, andWiley Online Library. Medical Subject Headings (MeSH) search terms included: large-scale data, big data, cardiovasculardiseases, prediction, machine-learning algorithms, artificial intelligence, and mortality. The search covered the period from2020 to 2024. Results: Of 262 retrieved articles, 16 were included. Three main themes were identified: large-scale data analysistechniques and machine-learning algorithms; applications of machine-learning algorithms and artificial intelligence inpredicting cardiovascular diseases; and the role of integrating large-scale data in disease prediction to improve the quality ofcare. Conclusions: While machine learning provides considerable opportunities for predicting CVD outcomes, limitationsremain. Machine-learning approaches are not always the most appropriate option, particularly in basic research where causalrelationships 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. Overcomingthese challenges will require interdisciplinary collaboration, methodological refinement, and further research.