An Enhanced Comprehensive Study Towards Predicting Cardiovascular Disease Using Machine Learning Techniques.
DOI:
https://doi.org/10.29207/joseit.v3i3.7585Keywords:
Healthcare, Machine Learning, Cardiovascular Disease, ML Algorithm, Ensemble TechniquesAbstract
In recent years, heart and cardiovascular diseases have become more common, causing a significant increase in mortality rates worldwide. Data from various organisations highlights the severity of heart disease, which remains a major concern. Accurately and quickly identifying severe conditions, such as heart disease, is vital for effective prevention. Techniques such as data mining, machine learning, and deep learning have been used in medicine to reliably detect heart disease. However, these methods depend on data that can change over time. To ensure accurate detection, proper use of historical data is essential; otherwise, results can be inaccurate. Machine learning techniques produce outcomes based on mathematical calculations, so data cleaning and refinement are necessary. Disease-related data can include text, numbers, and images, which may vary widely, requiring extensive stratification, normalisation, cleaning, encoding, and randomisation; otherwise, results may be biased. Our previous review article addressed a specific challenge related to the CVD Prediction Model. This Enhanced review primarily examines how machine learning techniques operate on medical datasets and their effectiveness in predicting cardiovascular diseases (CVD). It also aims to analyse datasets, features, and machine learning methods used in CVD prediction
References
H. E. Bays et al., “Ten things to know about ten cardiovascular disease risk factors,” Am J Prev Cardiol, vol. 5, p. 100149, Mar. 2021, doi: 10.1016/J.AJPC.2021.100149.
L. Ciumărnean et al., “Cardiovascular Risk Factors and Physical Activity for the Prevention of Cardiovascular Diseases in the Elderly,” Res. Public Health, vol. 19, p. 207, 2022, doi: 10.3390/ijerph19010207.
L. S. Mehta et al., “Cardiovascular Disease Risk Factors in Women: The Impact of Race and Ethnicity: A Scientific Statement from the American Heart Association,” Circulation, vol. 147, no. 19, pp. 1471–1487, May 2023, doi: 10.1161/CIR.0000000000001139/ASSET/65A233F7-3218-40CA-A84B-DA0CB4BAA410/ASSETS/GRAPHIC/CIR.0000000000001139.FIG02.JPG.
M. Swathy and K. Saruladha, “A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques,” ICT Express, vol. 8, no. 1, pp. 109–116, Mar. 2022, doi: 10.1016/j.icte.2021.08.021.
F. Zardawi, S. Gul, A. Abdulkareem, A. Sha, and J. Yates, “Association Between Periodontal Disease and Atherosclerotic Cardiovascular Diseases: Revisited,” Front Cardiovasc Med, vol. 7, p. 625579, Jan. 2021, doi: 10.3389/FCVM.2020.625579/BIBTEX.
A. K. Prajapati, U. K. Singh, and D. S. Yadav, “A Comprehensive Study and Analysis of the Working Process of Machine Learning (ML) Techniques to Predict Cardiovascular Disease,” pp. 293–306, 2025, doi: 10.1007/978-981-96-2182-8_22.
A. Dudchenko, M. Ganzinger, and G. Kopanitsa, “Machine Learning Algorithms in Cardiology Domain: A Systematic Review,” Open Bioinforma J, vol. 13, no. 1, pp. 25–40, Apr. 2020, doi: 10.2174/1875036202013010025.
K. P. Kresoja, M. Unterhuber, R. Wachter, H. Thiele, and P. Lurz, “A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction,” Basic Res Cardiol, vol. 118, no. 1, pp. 1–12, Dec. 2023, doi: 10.1007/S00395-023-00982-7/TABLES/2.
C. Krittanawong et al., “Machine learning prediction in cardiovascular diseases: a meta-analysis,” Scientific Reports 2020 10:1, vol. 10, no. 1, pp. 1–11, Sep. 2020, doi: 10.1038/s41598-020-72685-1.
M. A. Naser, A. A. Majeed, M. Alsabah, T. R. Al-Shaikhli, and K. M. Kaky, “A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges,” Algorithms 2024, Vol. 17, Page 78, vol. 17, no. 2, p. 78, Feb. 2024, doi: 10.3390/A17020078.
G. S. Rao and G. Muneeswari, “A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction,” EAI Endorsed Trans Pervasive Health Technol, vol. 10, Mar. 2024, doi: 10.4108/EETPHT.10.5411.
Y. Zhao, E. P. Wood, N. Mirin, S. H. Cook, and R. Chunara, “Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review,” Am J Prev Med, vol. 61, no. 4, pp. 596–605, Oct. 2021, doi: 10.1016/j.amepre.2021.04.016.
R. L. Sacco et al., “The Heart of 25 by 25: Achieving the Goal of Reducing Global and Regional Premature Deaths From Cardiovascular Diseases and Stroke: A Modeling Study From the American Heart Association and World Heart Federation,” Circulation, vol. 133, no. 23, pp. e674–e690, Jun. 2016, doi: 10.1161/CIR.0000000000000395/ASSET/E2F46EAA-3505-4141-BF71-3505B83573E6/ASSETS/GRAPHIC/E674FIG16.JPEG.
S. C. W. Tan, B.-B. Zheng, M.-L. Tang, H. Chu, Y.-T. Zhao, and C. Weng, “Global Burden of Cardiovascular Diseases and its Risk Factors, 1990-2021: A Systematic Analysis for the Global Burden of Disease Study 2021,” QJM, Jan. 2025, doi: 10.1093/QJMED/HCAF022.
S. S. Martin et al., “2025 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association,” Circulation, vol. 151, no. 8, pp. e41–e660, Feb. 2025, doi: 10.1161/CIR.0000000000001303,.
G. A. Mensah et al., “Global Burden of Cardiovascular Diseases and Risks, 1990-2022,” J Am Coll Cardiol, vol. 82, no. 25, p. 2350, Dec. 2023, doi: 10.1016/J.JACC.2023.11.007.
W. DeGroat, H. Abdelhalim, K. Patel, D. Mendhe, S. Zeeshan, and Z. Ahmed, “Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–13, Jan. 2024, doi: 10.1038/s41598-023-50600-8.
A. Ogunpola, F. Saeed, S. Basurra, A. M. Albarrak, and S. N. Qasem, “Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases,” Diagnostics 2024, Vol. 14, Page 144, vol. 14, no. 2, p. 144, Jan. 2024, doi: 10.3390/DIAGNOSTICS14020144.
D. B. Olawade, A. A. Soladoye, B. A. Omodunbi, N. Aderinto, and I. A. Adeyanju, “Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection,” Int J Cardiol, vol. 436, p. 133443, Oct. 2025, doi: 10.1016/J.IJCARD.2025.133443.
W. Alsabhan and A. Alfadhly, “Effectiveness of machine learning models in diagnosis of heart disease: a comparative study,” Sci Rep, vol. 15, no. 1, pp. 1–19, Dec. 2025, doi: 10.1038/S41598-025-09423-Y;SUBJMETA=166,639,985;KWRD=BIOMEDICAL+ENGINEERING,ENGINEERING.
D. Rohan, G. P. Reddy, Y. V. P. Kumar, K. P. Prakash, and C. P. Reddy, “An extensive experimental analysis for heart disease prediction using artificial intelligence techniques,” Sci Rep, vol. 15, no. 1, pp. 1–47, Dec. 2025, doi: 10.1038/S41598-025-90530-1;SUBJMETA=166,230,639,692,699,75,985;KWRD=BIOMEDICAL+ENGINEERING,HEART+FAILURE.
A. K. Prajapati and U. K. Singh, “An empirical analysis of ML techniques and/or algorithms for disease diagnosis prediction from the perspective of cardiovascular disease (CVD),” International Journal of Computing Algorithm, vol. 11, no. 2, Dec. 2022, doi: 10.20894/IJCOA.101.011.002.002.
A. K. P. Anil and U. K. Singh, “An Optimal Solution to the Overfitting and Underfitting Problem of Healthcare Machine Learning Models,” Journal of Systems Engineering and Information Technology (JOSEIT), vol. 2, no. 2, pp. 77–84, Oct. 2023, doi: 10.29207/JOSEIT.V2I2.5460.
A. K. Prajapati, U. K. Singh, R. Singh, and A. Shukla, “Study and analysis of feature selection problems and impact of bias in machine learning disease prediction models,” International Journal of Engineering & Technology, vol. 13, no. 2, pp. 182–188, May 2024, doi: 10.14419/HN1N3H15.
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