Risk Analysis in Cardiovascular Healthcare Systems Using Machine Learning for Better Clinical Decision Support

Michidmaa Arikhad; Aftab Tariq; Saad Rasool

doi:10.55524/ijircst.2026.14.2.5

Abstract

Cardiovascular disease is one of the leading causes of death worldwide. Many people develop heart problems without any early warning signs. Because of this, the risk analysis is very important in healthcare if it is correct and early. Traditional risk scores are often simple and incorporate a small number of factors. These methods may not be effective with all patients and populations. Machine learning has become a useful tool for cardiovascular risk analysis. It can research massive amounts of data regarding health and discover concealed patterns. Machine learning models can use data from electronic health records, medical images, ECG signals and wearable devices. Such models could be useful in predicting the risk of heart disease more accurately at an earlier stage and help doctors more accurately predict the risk of heart disease.This review paper is dedicated to study the recent work on machine learning methods for cardiovascular risk analysis. It discusses common sources of data, preprocessing techniques, and model types. Traditional machine learning models and deep learning methods are both discussed. The paper also describes how these models are applied in clinical decision support systems to assist doctors in making better decisions. In addition, this review raises important issues such as model validation, explainability, fairness and trust. Many challenges remain, however, including data quality issues as well as lack of real-world testing. Future research directions are also discussed, including explainable AI and privacy-preserving learning. Overall, this review demonstrates that machine learning has the potential to enhance cardiovascular risk analysis and aid in clinical decision-making. However, careful designing and clinical validation is required before widespread adoption.

Keywords

Cardiovascular Disease, Risk Analysis, Machine Learning, Clinical Decision Support, Healthcare Data, Explainable Artificial Intelligence

References

World Health Organization, “Cardiovascular diseases (CVDs),” World Health Organization, Jul. 31, 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
B. Chong et al., “Global burden of cardiovascular diseases: projections from 2025 to 2050,” European Journal of Preventive Cardiology, Sep. 2024. Available from: https://doi.org/10.1093/eurjpc/zwae281
Dumitrescu et al., “Cardiovascular Risk Factors in Socioeconomically Disadvantaged Populations,” Biomedicines, vol. 12, no. 9, pp. 1989–1989, Sep. 2024. Available from: https://doi.org/10.3390/biomedicines12091989
H. Phelan, V. Yates, and E. Lillie, “Challenges in healthcare delivery in low- and middle-income countries,” Anaesthesia & Intensive Care Medicine, vol. 23, no. 8, Aug. 2022. Available from: https://doi.org/10.1016/j.mpaic.2022.05.004
M. Amiri et al., “Cardiovascular disease risk prediction by Framingham risk score in women with polycystic ovary syndrome,” Reproductive Biology and Endocrinology, vol. 23, no. 1, Feb. 2025. Available from: https://doi.org/10.1186/s12958-025-01346-7
K. Batko and A. ?l?zak, “The Use of Big Data Analytics in Healthcare,” Journal of Big Data, vol. 9, pp. 1–24, Jan. 2022. Available from: https://doi.org/10.1186/s40537-021-00553-4
H. Habehh and S. Gohel, “Machine Learning in Healthcare,” Current Genomics, vol. 22, no. 4, pp. 291–300, Jul. 2021. Available from: https://doi.org/10.2174/1389202922666210705124359
Quer, R. Arnaout, M. Henne, and R. Arnaout, “Machine Learning and the Future of Cardiovascular Care,” Journal of the American College of Cardiology, vol. 77, no. 3, pp. 300–313, Jan. 2021. Available from: https://doi.org/10.1016/j.jacc.2020.11.030
N. Paltrinieri, L. Comfort, and G. Reniers, “Learning about risk: Machine learning for risk assessment,” Safety Science, vol. 118, pp. 475–486, Oct. 2019. Available from: https://doi.org/10.1016/j.ssci.2019.06.001
J. Li and X. Lu, “Cardiovascular risk assessment: The key path toward precision prevention,” Chronic Diseases and Translational Medicine, vol. 9, no. 4, pp. 273–276, Aug. 2023. Available from: https://doi.org/10.1002/cdt3.90
Johar et al., “Changes in 10-Year Predicted Cardiovascular Disease Risk for a Multiethnic Semirural Population in South East Asia,” JMIR Public Health and Surveillance, vol. 10, p. e55261, 2024. Available from: https://doi.org/10.2196/55261
M. Lloyd-Jones, M. G. Larson, A. Beiser, and D. Levy, “Lifetime risk of developing coronary heart disease,” The Lancet, vol. 353, no. 9147, pp. 89–92, Jan. 1999. Available from: https://doi.org/10.1016/s0140-6736(98)10279-9
J. Stewart, K. Addy, S. Campbell, and P. Wilkinson, “Primary prevention of cardiovascular disease,” JRSM Cardiovascular Disease, vol. 9, Jan. 2020. Available from: https://doi.org/10.1177/2048004020949326
S. P. Karunathilake and G. U. Ganegoda, “Secondary Prevention of Cardiovascular Diseases and Application of Technology for Early Diagnosis,” BioMed Research International, vol. 2018, pp. 1–9, May 2018. Available from: https://doi.org/10.1155/2018/5767864
D. Fuchs and P. K. Whelton, “High Blood Pressure and Cardiovascular Disease,” Hypertension, vol. 75, no. 2, pp. 285–292, Dec. 2019. Available from: https://doi.org/10.1161/HYPERTENSIONAHA.119.14240
M. Norouzzadeh et al., “Cigarette smoking and cardiovascular disease incidence and all-cause mortality,” BMC Public Health, vol. 24, no. 1, Apr. 2024. Available from: https://doi.org/10.1186/s12889-024-18468-z
Bazoukis et al., “Impact of Social Determinants of Health on Cardiovascular Disease,” Journal of the American Heart Association, vol. 14, no. 5, Mar. 2025. Available from: https://doi.org/10.1161/jaha.124.039031
W. M. Schultz et al., “Socioeconomic Status and Cardiovascular Outcomes,” Circulation, vol. 137, no. 20, pp. 2166–2178, 2018. Available from: https://doi.org/10.1161/circulationaha.117.029652
N. H. Alhumaidi et al., “The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction,” JMIR Medical Informatics, vol. 13, p. e68898, Jun. 2025. Available from: https://doi.org/10.2196/68898
Kowalewska et al., “Sources of patients' knowledge about cardiovascular disease prevention in Poland,” Advances in Interventional Cardiology, vol. 18, no. 1, pp. 27–33, Jan. 2022. Available from: https://doi.org/10.5114/aic.2022.116461
W. Wang, D. Ferrari, G. Haddon-Hill, and V. Curcin, “Electronic Health Records as Source of Research Data,” PubMed, 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK597466/
B. J. Wells, A. S. Nowacki, K. Chagin, and M. W. Kattan, “Strategies for Handling Missing Data in Electronic Health Record Derived Data,” eGEMs, vol. 1, no. 3, p. 7, Dec. 2013. Available from: https://doi.org/10.13063/2327-9214.1035
Jandera et al., “ECG Signal Analysis and Abnormality Detection Application,” Algorithms, vol. 18, no. 11, p. 689, Oct. 2025. Available from: https://doi.org/10.3390/a18110689
P. Z. Manoel et al., “Cardiovascular Imaging in the Era of Precision Medicine,” Health Science Reports, vol. 7, no. 11, Oct. 2024. Available from: https://doi.org/10.1002/hsr2.70173
Hughes et al., “Wearable Devices in Cardiovascular Medicine,” Circulation Research, vol. 132, no. 5, pp. 652–670, Mar. 2023. Available from: https://doi.org/10.1161/circresaha.122.322389
L. Ortiz, “Data Preprocessing Techniques for AI/ML-Readiness,” JMIR mHealth and uHealth, Apr. 2024. Available from: https://doi.org/10.2196/59587
S. B. Akter et al., “Optimizing stability of heart disease prediction across imbalanced learning,” Computer Methods and Programs in Biomedicine, vol. 265, p. 108702, Mar. 2025. Available from: https://doi.org/10.1016/j.cmpb.2025.108702
L. Hemdanou et al., “Comparative analysis of feature selection and extraction methods,” Computers and Education: Artificial Intelligence, vol. 7, p. 100301, Dec. 2024. Available from: https://doi.org/10.1016/j.caeai.2024.100301
S. Wan, F. Wan, and X. Dai, “Machine learning approaches for cardiovascular disease prediction: A review,” Archives of Cardiovascular Diseases, vol. 118, no. 10, May 2025. Available from: https://doi.org/10.1016/j.acvd.2025.04.055
Okolie et al., “Heart Disease Prediction: A Logistic Regression Approach,” Open Journal of Applied Sciences, vol. 15, no. 11, pp. 3534–3552, 2025. Available from: https://doi.org/10.4236/ojapps.2025.1511229
Luo, M. A. Arshad, and G. Luo, “Decision Trees for Strategic Choice of Augmenting Management Intuition with Machine Learning,” Symmetry, vol. 17, no. 7, p. 976, Jun. 2025. Available from: https://doi.org/10.3390/sym17070976
R. Khademi, “Deep learning models for predicting heart disease risk using the UCI database,” American Journal of Cardiovascular Disease, vol. 15, no. 6, pp. 374–386, 2025. Available from: https://doi.org/10.62347/cebk2916
S. Das, M. Epland, J. Yu, and R. Suri, “Interpretation of EKG with Image Recognition and Convolutional Neural Networks,” Current Problems in Cardiology, vol. 48, no. 8, p. 101744, Aug. 2023. Available from: https://doi.org/10.1016/j.cpcardiol.2023.101744
P. A. Moreno-Sánchez et al., “ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases,” Computers in Biology and Medicine, vol. 172, Feb. 2024. Available from: https://doi.org/10.1016/j.compbiomed.2024.108235
B. Naderalvojoud and T. Hernandez-Boussard, “Improving machine learning with ensemble learning on observational healthcare data,” AMIA Annual Symposium Proceedings, vol. 2023, p. 521, Jan. 2024. Available from: https://doi.org/10785929/pdf/146
S. Hossain et al., “Machine learning approach for predicting cardiovascular disease in Bangladesh,” BMC Cardiovascular Disorders, vol. 24, no. 1, Apr. 2024. Available from: https://doi.org/10.1186/s12872-024-03883-2
R. Sutton et al., “An overview of clinical decision support systems: Benefits, risks, and strategies for success,” NPJ Digital Medicine, vol. 3, no. 1, pp. 1–10, 2020. Available from: https://doi.org/10.1038/s41746-020-0221-y
M. Elhaddad and S. Hamam, “AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential,” Cureus, vol. 16, no. 4, Apr. 2024. Available from: https://doi.org/10.7759/cureus.57728
S. Miao et al., “The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases,” JMIR Medical Informatics, vol. 13, Mar. 2025. Available from: https://doi.org/10.2196/63186
S.-C. Chien et al., “Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review,” Healthcare, vol. 10, no. 4, p. 601, Mar. 2022. Available from: https://doi.org/10.3390/healthcare10040601
A. Gomez-Cabello et al., “AI-Based Clinical Decision Support Systems in Primary Care: A Scoping Review,” European Journal of Investigation in Health Psychology and Education, vol. 14, no. 3, pp. 685–698, Mar. 2024. Available from: https://doi.org/10.3390/ejihpe14030045
O. Asan, A. E. Bayrak, and A. Choudhury, “Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians,” Journal of Medical Internet Research, vol. 22, no. 6, Jun. 2020. Available from: https://doi.org/10.2196/15154
K. N. Singh and J. K. Mantri, “A clinical decision support system using rough set theory and machine learning,” Intelligent Medicine, vol. 4, no. 3, Apr. 2024. Available from: https://doi.org/10.1016/j.imed.2023.08.002
Y. Mao, B. L. Jimma, and T. B. Mihretie, “Machine learning algorithms for heart disease diagnosis: A systematic review,” Current Problems in Cardiology, vol. 50, no. 8, May 2025. Available from: https://doi.org/10.1016/j.cpcardiol.2025.103082
K. Hajian-Tilaki, “Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation,” Caspian Journal of Internal Medicine, vol. 4, no. 2, p. 627, 2024. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3755824/
Shreffler and M. R. Huecker, “Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios,” PubMed, Mar. 06, 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557491/
B. Van Calster et al., “Calibration: the Achilles heel of predictive analytics,” BMC Medicine, vol. 17, no. 1, Dec. 2019. Available from: https://doi.org/10.1186/s12916-019-1466-7
Wilimitis and C. G. Walsh, “Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care,” JMIR AI, vol. 2, no. 1, p. e49023, Dec. 2023. Available from: https://doi.org/10.2196/49023
Mavrogiorgos et al., “Bias in Machine Learning: A Literature Review,” Applied Sciences, vol. 14, no. 19, Oct. 2024. Available from: https://doi.org/10.3390/app14198860
Arora et al., “The value of standards for health datasets in AI-based applications,” Nature Medicine, vol. 29, no. 11, Oct. 2023. Available from: https://doi.org/10.1038/s41591-023-02608-w
Eini-Porat et al., “Tell me something interesting: Clinical utility of machine learning prediction models in the ICU,” Journal of Biomedical Informatics, vol. 132, Aug. 2022. Available from: https://doi.org/10.1016/j.jbi.2022.104107
Hammann and M. Wouters, “Explainability Versus Accuracy of Machine Learning Models,” European Accounting Review, Feb. 2025. Available from: https://doi.org/10.1080/09638180.2025.2463961
Pang, “A comparative study of explainable machine learning models with Shapley values for diabetes prediction,” Healthcare Analytics, vol. 7, Jun. 2025. Available from: https://doi.org/10.1016/j.health.2025.100390
S.-J. Lin et al., “Prediction Models Using Decision Tree and Logistic Regression Method for Predicting Hospital Revisits in Peritoneal Dialysis Patients,” Diagnostics, vol. 14, no. 6, p. 620, Jan. 2024. Available from: https://doi.org/10.3390/diagnostics14060620
Hanna et al., “Ethical and Bias Considerations in Artificial Intelligence/Machine Learning,” Modern Pathology, vol. 38, no. 3, 2024. Available from: https://doi.org/10.1016/j.modpat.2024.100686
T. Pham, “Ethical and legal considerations in healthcare AI,” Royal Society Open Science, vol. 12, no. 5, May 2025. Available from: https://doi.org/10.1098/rsos.241873
J. Yao et al., “Trust transfer from medical AI to doctors and hospitals,” BMC Medical Ethics, vol. 26, no. 1, Oct. 2025. Available from: https://doi.org/10.1186/s12910-025-01300-7
S. Mohammed and N. Malhotra, “Ethical and Regulatory Challenges in Machine Learning-Based Healthcare Systems,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations, vol. 5, no. 1, May 2025. Available from: https://doi.org/10.1016/j.tbench.2025.100215
K. Rasheed et al., “Explainable, trustworthy, and ethical machine learning for healthcare: A survey,” Computers in Biology and Medicine, vol. 149, Oct. 2022. Available from: https://doi.org/10.1016/j.compbiomed.2022.106043
J. Digitale et al., “Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models,” JMIR Medical Informatics, vol. 13, Nov. 2025. Available from: https://doi.org/10.2196/79307
Swinckels et al., “The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases,” Journal of Medical Internet Research, Apr. 2023. Available from: https://doi.org/10.2196/48320

Cites this article as

M. Arikhad, A. Tariq, S. Rasool, "Risk Analysis in Cardiovascular Healthcare Systems Using Machine Learning for Better Clinical Decision Support", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-14, Issue-2, Page No-34-44, 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.2.5

Corresponding Author

Michidmaa Arikhad

Department of Computer Science, American National University, Louisville Kentucky, USA

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