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@article{207393,
        author = {Thanuja.K and Kruthika .V.T},
        title = {Machine Learning-Driven Cardiovascular Risk Assessment Using Big Data Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1533-1539},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207393},
        abstract = {cardiovascular diseases (CVDs) continue to be the most common causes of death around the globe, leading to about 17.9 million fatalities each year. Traditional approaches towards the diagnosis and CVD risk prediction have proven to be somewhat effective but still reactive with limitations associated with not being able to work with large-scale heterogeneous datasets. The current study introduces an innovative approach to CVDs risk prediction based on big data analysis and implementation of machine learning algorithms. The novel predictive model utilizes multiple data sources such as EHRs, wearable devices metrics, genomics data, and medical imaging to conduct a full-scale risk assessment. Multiple machine learning algorithms have been employed, including Random Forest, XGBoost, Support Vector Machines (SVM), and Deep Neural Networks, with performance tested on a consolidated sample of 919 patients diagnosed with CVDs, 17.6% of whom have survived. Experimental evaluation showed that the best performance was demonstrated by Random Forest with AUC = 0.934, accuracy = 93.72% and precision = 0.91%. Proposed approach successfully solves the problems such as the integration of multiple heterogeneous data sources, handling of missing data (for 3 variables with more than 5% of missing data) and real-time risk stratification.},
        keywords = {Big Data Analytics, Machine Learning, Cardiovascular Disease Prediction, Personalized Medicine, Electronic Health Records, Random Forest, Predictive Analytics, Healthcare Informatics},
        month = {August},
        }