Home / Current Issue / Paper 1702780
Machine Learning Models for Early Detection of Cardiovascular Diseases: A Systematic Review
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide, emphasizing the need for early detection to improve patient outcomes and reduce healthcare costs. Machine learning (ML) has emerged as a transformative tool for predicting and diagnosing CVDs by leveraging vast datasets, including electronic health records (EHRs), medical imaging, wearable device data, and genomic information. This systematic review explores the latest advancements in ML models for early CVD detection, highlighting key algorithms, data sources, and evaluation metrics. Supervised learning models such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and Gradient Boosting have shown promise in risk prediction, while deep learning techniques, including Convolutional Neural Networks (CNN) for imaging analysis and Long Short-Term Memory (LSTM) networks for time-series data, enhance diagnostic accuracy. Additionally, feature selection and engineering methods improve the predictive performance of ML models by identifying critical risk factors from structured and unstructured data. Despite significant progress, challenges remain, including data quality issues, model interpretability, generalizability across diverse populations, and regulatory compliance with healthcare standards such as GDPR and HIPAA. Bias in ML models and concerns over patient privacy must also be addressed to ensure ethical deployment. Future research should focus on integrating ML with personalized medicine, federated learning for secure data sharing, and real-time monitoring through IoT-enabled devices. Developing explainable AI models and robust regulatory frameworks will further enhance clinical adoption and patient trust. This review underscores the potential of ML in revolutionizing early CVD detection and provides insights for researchers, clinicians, and policymakers to harness AI-driven innovations for improving cardiovascular health outcomes.
Keywords
Machine Learning, Cardiovascular Diseases, Early Detection, Deep Learning, Predictive Analytics, Healthcare AI, Electronic Health Records
References
[1] Adelodun, A.M., Adekanmi, A.J., Roberts, A. and Adeyinka, A.O., 2018. Effect of asymptomatic malaria parasitemia on the uterine and umbilical artery blood flow impedance in thirdtrimester singleton Southwestern Nigerian pregnant women. Tropical Journal of Obstetrics and Gynaecology, 35(3), pp.333-341.
[2] Adewoyin, M.A., 2021. Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry.
[3] Aggrawal, R. and Pal, S., 2021. Elimination and backward selection of features (P-value technique) in prediction of heart disease by using machine learning algorithms. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), pp.2650-2665.
[4] Agho, G., Ezeh, M.O., Isong, M., Iwe, D. and Oluseyi, K.A., 2021. Sustainable pore pressure prediction and its impact on geo-mechanical modelling for enhanced drilling operations. World Journal of Advanced Research and Reviews, 12(1), pp.540-557.
[5] Ahmed, Z., Mohamed, K., Zeeshan, S. and Dong, X., 2020. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, p.baaa010.
[6] Ajayi, A. and Akerele, J.I., 2021. A high-impact data-driven decisionmaking model for integrating cutting-edge cybersecurity strategies into public policy, governance, and organizational frameworks. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.623-637.
[7] Akinade, A.O., Adepoju, P.A., Ige, A.B., Afolabi, A.I. and Amoo, O.O., 2021. A conceptual model for network security automation: Leveraging AI-driven frameworks to enhance multi-vendor infrastructure resilience. International Journal of Science and Technology Research Archive, 1(1), pp.39-59.
[8] Alli, O.I. and Dada, S.A., 2021. Innovative Models for Tobacco Dependency Treatment: A Review of Advances in Integrated Care Approaches in High-Income Healthcare Systems. IRE Journals, 5(6), pp.273-282.
[9] Atasoy, S., Johar, H., Peters, A. and Ladwig, K.H., 2019. Association of hypertension cut-off values with 10-year cardiovascular mortality and clinical consequences: a real-world perspective from the prospective MONICA/KORA study. European heart journal, 40(9), pp.732-738.
[10] Atta, J.A., Al Zoubi, M.A.M., Temedie-Asogwa, T. and Amafah, J., Comparing the Cost-Effectiveness of Pharmaceutical vs. Non-Pharmaceutical Interventions for Diabetes Management.
[11] Austin-Gabriel, B., Hussain, N.Y., Ige, A.B., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks. Open Access Research Journal of Engineering and Technology, 1(01), pp.047-055.
[12] Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C. and Elmaghraby, A., 2020. Ensemble deep learning models for heart disease classification: A case study from Mexico. Information, 11(4), p.207.
[13] Bayyapu, S., Turpu, R.R. and Vangala, R.R., 2019. Advancing healthcare decision-making: The fusion of machine learning, predictive analytics, and cloud technology. International Journal of Computer Engineering and Technology (IJCET), 10(5), pp.157-170.
[14] Bidemi, A.I., Oyindamola, F.O., Odum, I., Stanley, O.E., Atta, J.A., Olatomide, A.M., Nnamdi, N.C., Amafah, J. and Helen, O.O., 2021. Challenges Facing Menstruating Adolescents: A Reproductive Health Approach. Journal of Adolescent Health, 68(5), pp.1-10.
[15] Bowers, A.J. and Zhou, X., 2019. Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), pp.20-46.
[16] Chen, S., Qi, J., Fan, S., Qiao, Z., Yeo, J.C. and Lim, C.T., 2021. Flexible wearable sensors for cardiovascular health monitoring. Advanced Healthcare Materials, 10(17), p.2100116.
[17] Dienagha, I.N., Onyeke, F.O., Digitemie, W.N. and Adekunle, M., 2021. Strategic reviews of greenfield gas projects in Africa: Lessons learned for expanding regional energy infrastructure and security.
[18] Dirlikov, E., Jahun, I., Odafe, S.F., Obinna, O., Onyenuobi, C., Ifunanya, M., Efuntoye, T.A., Tingir, N., Ene, U., Fagbemi, A. and Meribe, C., 2021. Section navigation rapid scale-up of an antiretroviral therapy program before and during the COVID-19 pandemic-nine states, Nigeria, March 31, 2019-September 30, 2020.
[19] Du, Z., Yang, Y., Zheng, J., Li, Q., Lin, D., Li, Y., Fan, J., Cheng, W., Chen, X.H. and Cai, Y., 2020. Accurate prediction of coronary heart disease for patients with hypertension from electronic health records with big data and machine-learning methods: model development and performance evaluation. JMIR medical informatics, 8(7), p.e17257.
[20] Effrosynidis, D. and Arampatzis, A., 2021. An evaluation of feature selection methods for environmental data. Ecological Informatics, 61, p.101224.
[21] Elagizi, A., Kachur, S., Carbone, S., Lavie, C.J. and Blair, S.N., 2020. A review of obesity, physical activity, and cardiovascular disease. Current obesity reports, 9, pp.571-581.
[22] Elujide, I., Fashoto, S.G., Fashoto, B., Mbunge, E., Folorunso, S.O. and Olamijuwon, J.O., 2021. Informatics in Medicine Unlocked.
[23] Elujide, I., Fashoto, S.G., Fashoto, B., Mbunge, E., Folorunso, S.O. and Olamijuwon, J.O., 2021. Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases. Informatics in Medicine Unlocked, 23, p.100545.
[24] Faizal, A.S.M., Thevarajah, T.M., Khor, S.M. and Chang, S.W., 2021. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Computer methods and programs in biomedicine, 207, p.106190.
[25] Grant, R.W., McCloskey, J., Hatfield, M., Uratsu, C., Ralston, J.D., Bayliss, E. and Kennedy, C.J., 2020. Use of latent class analysis and k-means clustering to identify complex patient profiles. JAMA network open, 3(12), pp.e2029068-e2029068.
[26] Hassan, Y.G., Collins, A., Babatunde, G.O., Alabi, A.A. and Mustapha, S.D., 2021. AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks. Artificial intelligence (AI), p.16.
[27] Hurley, N.C., Spatz, E.S., Krumholz, H.M., Jafari, R. and Mortazavi, B.J., 2020. A survey of challenges and opportunities in sensing and analytics for risk factors of cardiovascular disorders. ACM transactions on computing for healthcare, 2(1), pp.1-42.
[28] Hussain, N.Y., Austin-Gabriel, B., Ige, A.B., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. AI-driven predictive analytics for proactive security and optimization in critical infrastructure systems. Open Access Research Journal of Science and Technology, 2(02), pp.006-015.
[29] Ike, C.C., Ige, A.B., Oladosu, S.A., Adepoju, P.A., Amoo, O.O. and Afolabi, A.I., 2021. Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement. Magna Scientia Advanced Research and Reviews, 2(1), pp.074-086.
[30] Jahun, I., Dirlikov, E., Odafe, S., Yakubu, A., Boyd, A.T., Bachanas, P., Nzelu, C., Aliyu, G., Ellerbrock, T., Swaminathan, M. and CDC Nigeria ART Surge Team, 2021. Ensuring optimal community HIV testing services in Nigeria using an enhanced community case-finding package (ECCP), October 2019–March 2020: acceleration to HIV epidemic control. HIV/AIDS-Research and Palliative Care, pp.839-850.
[31] Jahun, I., Said, I., El-Imam, I., Ehoche, A., Dalhatu, I., Yakubu, A., Greby, S., Bronson, M., Brown, K., Bamidele, M. and Boyd, A.T., 2021. Optimizing community linkage to care and antiretroviral therapy Initiation: Lessons from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) and their adaptation in Nigeria ART Surge. PLoS One, 16(9), p.e0257476.
[32] Kalusivalingam, A.K., Sharma, A., Patel, N. and Singh, V., 2021. Enhancing Diagnostic Accuracy in Medical Imaging through Convolutional Neural Networks and Transfer Learning Algorithms. International Journal of AI and ML, 2(3).
[33] Khosrow Tayebati, S., Ejike Nwankwo, I. and Amenta, F., 2013. Intranasal drug delivery to the central nervous system: present status and future outlook. Current pharmaceutical design, 19(3), pp.510-526.
[34] Khosrow Tayebati, S., Tomassoni, D., Ejike Nwankwo, I., Di Stefano, A., Sozio, P., Serafina Cerasa, L. and Amenta, F., 2013. Modulation of monoaminergic transporters by choline-containing phospholipids in rat brain. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders), 12(1), pp.94-103.
[35] Kothinti, R.R., 2021. Advancements in natural language processing for automated phenotyping and predictive analytics in oncology EHRS. Iconic Research and Engineering Journals, 8(8), pp.245-252.
[36] Kuo, Y.M., Nwankwo, E.I., Nussbaum, R.L., Rogers, J. and Maccecchini, M.L., 2019. Translational inhibition of α-synuclein by Posiphen normalizes distal colon motility in transgenic Parkinson mice. American journal of neurodegenerative disease, 8(1), p.1.
[37] Lowres, N., Duckworth, A., Redfern, J., Thiagalingam, A. and Chow, C.K., 2020. Use of a machine learning program to correctly triage incoming text messaging replies from a cardiovascular text–based secondary prevention program: feasibility study. JMIR mHealth and uHealth, 8(6), p.e19200.
[38] Mathur, P., Srivastava, S., Xu, X. and Mehta, J.L., 2020. Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, p.1179546820927404.
[39] Matthew, A., Opia, F.N., Matthew, K.A., Kumolu, A.F. and Matthew, T.F., 2021. Cancer Care Management in the COVID-19 Era: Challenges and adaptations in the global south. Cancer, 2(6).
[40] Matthew, K.A., AKINWALE, F.M., OPIA, F.N. and Adenike, A., 2021. The Relationship between oral Contraceptive Use, Mammographic Breast Density, and Breast Cancer Risk.
[41] Mohammed, E.M. and Osman, E.G.A., 2021, February. Comparison between neural networks and binary logistic regression for classification observation (case study: risk factors for cardiovascular disease). In 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) (pp. 1-6). IEEE.
[42] Nasuti, C., Falcioni, M.L., Nwankwo, I.E., Cantalamessa, F. and Gabbianelli, R., 2008. Effect of permethrin plus antioxidants on locomotor activity and striatum in adolescent rats. Toxicology, 251(1-3), pp.45-50.
[43] Nissa, N., Jamwal, S. and Mohammad, S., 2020. Early detection of cardiovascular disease using machine learning techniques an experimental study. Int. J. Recent Technol. Eng, 9(3), pp.635-641.
[44] Nti, I.K., Nyarko-Boateng, O. and Aning, J., 2021. Performance of machine learning algorithms with different K values in K-fold cross-validation. International Journal of Information Technology and Computer Science, 13(6), pp.61-71.
[45] Nwaozomudoh, M.O., Odio, P.E., Kokogho, E., Olorunfemi, T.A., Adeniji, I.E. and Sobowale, A., 2021. Developing a conceptual framework for enhancing interbank currency operation accuracy in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.481-494.
[46] Odio, P.E., Kokogho, E., Olorunfemi, T.A., Nwaozomudoh, M.O., Adeniji, I.E. and Sobowale, A., 2021. Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.495-507.
[47] Oladosu, S.A., Ike, C.C., Adepoju, P.A., Afolabi, A.I., Ige, A.B. and Amoo, O.O., 2021. The future of SD-WAN: A conceptual evolution from traditional WAN to autonomous, self-healing network systems. Magna Scientia Advanced Research and Reviews.
[48] Olamijuwon, O.J., 2020. Real-time Vision-based Driver Alertness Monitoring using Deep Neural Network Architectures (Master's thesis, University of the Witwatersrand, Johannesburg (South Africa)).
[49] Oluokun, O.A., 2021. Design of a Power System with Significant Mass and Volume Reductions, Increased Efficiency, and Capability for Space Station Operations Using Optimization Approaches (Doctoral dissertation, McNeese State University).
[50] Oyedokun, O.O., 2019. Green human resource management practices and its effect on the sustainable competitive edge in the Nigerian manufacturing industry (Dangote) (Doctoral dissertation, Dublin Business School).
[51] Priyanka and Kumar, D., 2020. Decision tree classifier: a detailed survey. International Journal of Information and Decision Sciences, 12(3), pp.246-269.
[52] Rudski, L., Januzzi, J.L., Rigolin, V.H., Bohula, E.A., Blankstein, R., Patel, A.R., Bucciarelli-Ducci, C., Vorovich, E., Mukherjee, M., Rao, S.V. and Beanlands, R., 2020. Multimodality imaging in evaluation of cardiovascular complications in patients with COVID-19: JACC scientific expert panel. Journal of the American College of Cardiology, 76(11), pp.1345-1357.
[53] Sajid, M.R., Muhammad, N., Zakaria, R., Shahbaz, A., Bukhari, S.A.C., Kadry, S. and Suresh, A., 2021. Nonclinical features in predictive modeling of cardiovascular diseases: a machine learning approach. Interdisciplinary Sciences: Computational Life Sciences, 13, pp.201-211.
[54] Salem, N. and Hussein, S., 2019. Data dimensional reduction and principal components analysis. Procedia Computer Science, 163, pp.292-299.
[55] Serhani, M.A., T. El Kassabi, H., Ismail, H. and Nujum Navaz, A., 2020. ECG monitoring systems: Review, architecture, processes, and key challenges. Sensors, 20(6), p.1796.
[56] Strianese, O., Rizzo, F., Ciccarelli, M., Galasso, G., D’Agostino, Y., Salvati, A., Del Giudice, C., Tesorio, P. and Rusciano, M.R., 2020. Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes, 11(7), p.747.
[57] Suryadevara, S. and Yanamala, A.K.Y., 2020. Fundamentals of Artificial Neural Networks: Applications in Neuroscientific Research. Revista de Inteligencia Artificial en Medicina, 11(1), pp.38-54.
[58] Teixeira, E., Fonseca, H., Diniz-Sousa, F., Veras, L., Boppre, G., Oliveira, J., Pinto, D., Alves, A.J., Barbosa, A., Mendes, R. and Marques-Aleixo, I., 2021. Wearable devices for physical activity and healthcare monitoring in elderly people: a critical review. Geriatrics, 6(2), p.38.
[59] Tomassoni, D., Amenta, F., Amantini, C., Farfariello, V., Di Cesare Mannelli, L., Nwankwo, I.E., Marini, C. and Tayebati, S.K., 2013. Brain activity of thioctic acid enantiomers: in vitro and in vivo studies in an animal model of cerebrovascular injury. International Journal of Molecular Sciences, 14(3), pp.4580-4595.
[60] Tomassoni, D., Amenta, F., Di Cesare Mannelli, L., Ghelardini, C., Nwankwo, I.E., Pacini, A. and Tayebati, S.K., 2013. Neuroprotective activity of thioctic acid in central nervous system lesions consequent to peripheral nerve injury. BioMed Research International, 2013(1), p.985093.
[61] Tomassoni, D., Catalani, A., Cinque, C., Antonietta Di Tullio, M., Khosrow Tayebati, S., Cadoni, A., Ejike Nwankwo, I., Traini, E. and Amenta, F., 2012. Effects of cholinergic enhancing drugs on cholinergic transporters in the brain and peripheral blood lymphocytes of spontaneously hypertensive rats. Current Alzheimer Research, 9(1), pp.120-127.
[62] Tomassoni, D., Nwankwo, I.E., Gabrielli, M.G., Bhatt, S., Muhammad, A.B., Lokhandwala, M.F., Tayebati, S.K. and Amenta, F., 2013. Astrogliosis in the brain of obese Zucker rat: a model of metabolic syndrome. Neuroscience letters, 543, pp.136-141.
[63] Xie, L., Li, Z., Zhou, Y., He, Y. and Zhu, J., 2020. Computational diagnostic techniques for electrocardiogram signal analysis. Sensors, 20(21), p.6318.
How to cite this paper
@article{1702780,
author = {Damilola Osamika, Bamidele Samuel Adelusi, MariaTheresa Chinyeaka Kelvin-Agwu, Ashiata Yetunde Mustapha, Nura Ikhalea},
title = {Machine Learning Models for Early Detection of Cardiovascular Diseases: A Systematic Review},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {12},
pages = {355-368},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702780.pdf},
abstract = {Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide, emphasizing the need for early detection to improve patient outcomes and reduce healthcare costs. Machine learning (ML) has emerged as a transformative tool for predicting and diagnosing CVDs by leveraging vast datasets, including electronic health records (EHRs), medical imaging, wearable device data, and genomic information. This systematic review explores the latest advancements in ML models for early CVD detection, highlighting key algorithms, data sources, and evaluation metrics. Supervised learning models such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and Gradient Boosting have shown promise in risk prediction, while deep learning techniques, including Convolutional Neural Networks (CNN) for imaging analysis and Long Short-Term Memory (LSTM) networks for time-series data, enhance diagnostic accuracy. Additionally, feature selection and engineering methods improve the predictive performance of ML models by identifying critical risk factors from structured and unstructured data. Despite significant progress, challenges remain, including data quality issues, model interpretability, generalizability across diverse populations, and regulatory compliance with healthcare standards such as GDPR and HIPAA. Bias in ML models and concerns over patient privacy must also be addressed to ensure ethical deployment. Future research should focus on integrating ML with personalized medicine, federated learning for secure data sharing, and real-time monitoring through IoT-enabled devices. Developing explainable AI models and robust regulatory frameworks will further enhance clinical adoption and patient trust. This review underscores the potential of ML in revolutionizing early CVD detection and provides insights for researchers, clinicians, and policymakers to harness AI-driven innovations for improving cardiovascular health outcomes.},
keywords = {Machine Learning, Cardiovascular Diseases, Early Detection, Deep Learning, Predictive Analytics, Healthcare AI, Electronic Health Records},
month = {June},
}