Artificial intelligence-driven predictive analytics for early detection and risk stratification of cardiovascular disease
DOI:
https://doi.org/10.18203/2320-6012.ijrms20262652Keywords:
Artificial Intelligence, Machine learning, Deep learning, Predictive analytics, Cardiovascular disease, Risk stratification, Early detection, Precision medicineAbstract
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, necessitating improved approaches for early detection and risk stratification. Traditional cardiovascular risk assessment models often demonstrate limited predictive accuracy because they rely on a restricted number of clinical variables and linear statistical relationships. Artificial intelligence (AI)-driven predictive analytics has emerged as a promising solution capable of processing large, complex, and multidimensional healthcare datasets to improve cardiovascular risk prediction and clinical decision-making. This systematic review aimed to evaluate the effectiveness of AI-based predictive models for the early detection and risk stratification of CVD. A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Embase, CINAHL, IEEE Xplore, and the Cochrane Library for studies published between January 2015 and December 2025. A total of 130 studies involving approximately 12.8 million participants met the eligibility criteria and were included in the review. The findings demonstrated that machine learning (ML) and deep learning (DL) models consistently outperformed conventional cardiovascular risk prediction tools, achieving area under the receiver operating characteristic curve (AUC) values ranging from 0.80-0.99 across various cardiovascular conditions. The strongest evidence was observed in the prediction of coronary artery disease, heart failure, atrial fibrillation, stroke, and major adverse cardiovascular events. Electronic health records, electrocardiography, medical imaging, wearable devices, and multimodal datasets were the most commonly utilized data sources. Despite their promising performance, challenges related to external validation, interpretability, algorithmic bias, and clinical implementation remain.
References
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Satam G, Pellikka PA, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70-4.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs. Nat Biomed Eng. 2018;2(3):158-64.
Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Cognitive machine-learning algorithm for cardiac imaging. JACC Cardiovasc Imaging. 2016;9(11):1339-47.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement. BMJ. 2021;372:n71.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-8.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). Ann Intern Med. 2015;162(1):55-63.
Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population. BMJ. 2016;353:i2416.
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58.
Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8.
Shameer K, Johnson KW, Yahi A, Miotto R, Li L, Ricks D, et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning. NPJ Digit Med. 2018;1:49.
Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
Obermeyer Z, Emanuel EJ. Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy. JAMA. 2016;316(22):2402-10.
Ribeiro AH, Ribeiro MH, Paixão GMM, Oliveira DM, Gomes PR, Canazart JA, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1760.
Tison GH, Sanchez JM, Ballinger B, Singh A, Olgin JE, Pletcher MJ, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol. 2018;3(5):409-16.
Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, et al. Rationale and design of a large-scale smartwatch study. Am Heart J. 2019;207:66-75.
Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for identification of patients with atrial fibrillation during sinus rhythm. Lancet. 2019;394(10201):861-7.
Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252-6.
Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation using deep learning. Circulation. 2018;138(16):1623-35.
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633-41.
Al'Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease. J Am Coll Cardiol. 2019;73(11):1317-35.
Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP, Krumholz HM. Comparison of machine learning methods with national cardiovascular data registry models. Circ Cardiovasc Qual Outcomes. 2016;9(6):629-37.
Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, et al. Machine learning to predict mortality in heart failure. Circ Heart Fail. 2018;11(7):e005181.
Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-day mortality after heart failure hospitalization using machine learning. Circ Cardiovasc Qual Outcomes. 2017;10(7):e003114.
Kwon JM, Kim KH, Jeon KH, Park J. Deep learning for predicting in-hospital mortality among heart failure patients. ESC Heart Fail. 2019;6(6):1259-67.
Lee H, Yoon SB, Yang SM, Kim WH, Ryu HG, Jung CW, et al. Prediction of acute kidney injury and mortality using machine learning. Crit Care. 2018;22(1):254.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies. Nat Med. 2018;24(11):1716-20.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review and opportunities. Brief Bioinform. 2018;19(6):1236-46.
Avati A, Jung K, Harman S, Downing L, Ng A, Shah NH. Improving palliative care with deep learning. BMC Med Inform Decis Mak. 2018;18(4):122.
Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Allen NB, et al. Performance of machine learning models for cardiovascular risk prediction. J Am Heart Assoc. 2020;9(22):e017494.
Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction by machine learning. Circ Res. 2017;121(9):1092-101.
Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep learning for prediction of obstructive coronary artery disease. JACC Cardiovasc Imaging. 2018;11(11):1654-64.
Diller GP, Kempny A, Alonso-Gonzalez R, Swan L, Uebing A, Li W, et al. Machine learning algorithms estimating prognosis in cardiovascular disease. Eur Heart J. 2019;40(13):1069-77.
Adler ED, Voors AA, Klein L, Macheret F, Braun OO, Urey MA, et al. Improving risk prediction in heart failure using machine learning. Eur J Heart Fail. 2020;22(1):139-47.
Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine. Circ Res. 2018;122(11):1525-40.
Ribeiro AH, Paixão GMM, Gomes PR, Ribeiro MH, Canazart JA, Ferreira MP, et al. Telehealth ECG diagnosis using deep neural networks. Nat Commun. 2020;11(1):1760.
Pencina MJ, Peterson ED. Moving from clinical trials to precision cardiovascular medicine. JAMA. 2016;315(16):1713-4.
Deo SV, Deo RC. Machine learning and cardiovascular disease prediction. Heart. 2021;107(6):449-55.
Kwon JM, Lee Y, Lee Y, Lee S, Park J. Explainable artificial intelligence in cardiovascular medicine. Korean Circ J. 2022;52(5):327-41.
Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke statistics-2023 update. Circulation. 2023;147(8):e93-621.
Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics-2021 update. Circulation. 2021;143(8):e254-743.
Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2019 update. Circulation. 2019;139(10):e56-28.
Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, et al. 2021 AHA/ACC chest pain guideline. Circulation. 2021;144(22):e368-454.
McCarthy CP, Vaduganathan M, McCarthy KJ, Januzzi JL, Bhatt DL, McEvoy JW. Machine learning and cardiovascular medicine. J Am Coll Cardiol. 2021;77(3):303-13.
Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, et al. MIMIC-III clinical database. Sci Data. 2016;3:160035.
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-6.
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31.
Ahmad MA, Eckert C, Teredesai A. Interpretable machine learning in healthcare. Proc ACM Int Conf Bioinform Comput Biol Health Inform. 2018;1:559-60.
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765-74.
Ribeiro MT, Singh S, Guestrin C. "Why should I trust you?": Explaining the predictions of any classifier. Proc ACM SIGKDD Int Conf Knowl Discov Data Min. 2016;2:1135-44.
Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577-9.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with AI. BMC Med. 2019;17(1):195.
Beam AL, Manrai AK, Ghassemi M. Challenges to the reproducibility of machine learning models in health care. JAMA. 2020;323(4):305-6.
Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745-50.
Sendak MP, D'Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020;4(1):45-52.
Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella NCF, Halpern A, et al. Human–computer collaboration for skin cancer recognition. Nat Med. 2020;26(8):1229-34.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in healthcare algorithms. Science. 2019;366(6464):447-53.
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