Key result
Big data analytics models using artificial intelligence and machine learning can improve cardiovascular disease prediction, risk stratification, and personalized medicine development.
Why the study?
The study was conducted to evaluate existing big data analytics models used for CVD prediction and critically analyse the pitfalls and advantages of models using artificial intelligence, machine learning, and artificial neural networks.
Big data analytics and machine learning offer significant potential for improving cardiovascular disease prediction, risk stratification, and personalized medicine.
The term "big data analytics (BDA)" defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems.
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Sharma et al. (2025) conducted a review in Cardiovascular diseases. Big data analytics was evaluated. Big data analytics models using artificial intelligence and machine learning can improve cardiovascular disease prediction, risk stratification, and personalized medicine development.
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