{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:17:34Z","timestamp":1775794654771,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Artificial intelligence (AI) is playing a dominant role in advancing heart failure detection and diagnosis, significantly furthering personalized healthcare. This review synthesizes AI-driven innovations by examining methodologies, applications, and outcomes. We investigate the integration of machine learning algorithms, diverse datasets including electronic health records (EHRs), medical records, imaging data, and clinical notes, deep learning models, and neural networks to enhance diagnostic accuracy. Key advancements include prediction models that leverage real-time data from wearable devices alongside state-of-the-art AI systems trained on patient data from hospitals and clinics. Notably, recent studies have reported diagnostic accuracies ranging from 86.7% to as high as 99.9%, with sensitivity and specificity values often exceeding 97%, underscoring the potential of these AI systems to improve early detection and clinical decision-making substantially. Our review further explores the impact of symmetry and asymmetry in model design, highlighting that symmetric architectures like U-Net offer computational efficiency and structured feature extraction. In contrast, asymmetric models improve the sensitivity to rare conditions and subtle clinical patterns. Incorporating these deep learning (DL) methods in anomaly detection and disease progression modeling further reinforces their positive impact on diagnostic accuracy and patient outcomes. Furthermore, this review identifies challenges in current AI applications, such as data quality, algorithmic transparency, model bias, and evaluation metrics, while outlining future research directions, including integrating generative models, hybrid architectures, and explainable AI techniques to optimize clinical practice.<\/jats:p>","DOI":"10.3390\/sym17030469","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T07:59:54Z","timestamp":1742457594000},"page":"469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["AI-Driven Technology in Heart Failure Detection and Diagnosis: A Review of the Advancement in Personalized Healthcare"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1327-3683","authenticated-orcid":false,"given":"Ikteder Akhand","family":"Udoy","sequence":"first","affiliation":[{"name":"Department of Computing, Boise State University, Boise, ID 83725, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1080-3398","authenticated-orcid":false,"given":"Omiya","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Boise State University, Boise, ID 83725, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1016\/j.jacc.2021.06.017","article-title":"Race, ethnicity, hypertension, and heart disease: JACC focus seminar 1\/9","volume":"78","author":"Ogunniyi","year":"2021","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","first-page":"e46486","article-title":"Advancements in heart failure management: A comprehensive narrative review of emerging therapies","volume":"15","author":"Sapna","year":"2023","journal-title":"Cureus"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1161\/CIRCRESAHA.116.308398","article-title":"Stroke risk factors, genetics, and prevention","volume":"120","author":"Boehme","year":"2017","journal-title":"Circ. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e535","DOI":"10.1161\/CIR.0000000000000450","article-title":"Contributory risk and management of comorbidities of hypertension, obesity, diabetes mellitus, hyperlipidemia, and metabolic syndrome in chronic heart failure: A scientific statement from the American Heart Association","volume":"134","author":"Bozkurt","year":"2016","journal-title":"Circulation"},{"key":"ref_5","unstructured":"World Health Organization (2025, March 05). Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country, 2000\u20132019. Available online: https:\/\/www.who.int\/data\/gho\/data\/themes\/mortality-and-global-health-estimates."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1093\/cvr\/cvac013","article-title":"Global burden of heart failure: A comprehensive and updated review of epidemiology","volume":"118","author":"Savarese","year":"2022","journal-title":"Cardiovasc. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1002\/ejhf.1858","article-title":"Epidemiology of heart failure","volume":"22","author":"Groenewegen","year":"2020","journal-title":"Eur. J. Heart Fail."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/ehf2.12005","article-title":"Heart failure: Preventing disease and death worldwide","volume":"1","author":"Ponikowski","year":"2014","journal-title":"ESC Heart Fail."},{"key":"ref_9","unstructured":"Dattani, S., Samborska, V., Ritchie, H., and Roser, M. (2025, March 05). Cardiovascular Diseases. Available online: https:\/\/ourworldindata.org\/cardiovascular-diseases."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nazar, W., Nazar, K., and Dani\u0142owicz-Szymanowicz, L. (2024). Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality. Life, 14.","DOI":"10.3390\/life14060761"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e273","DOI":"10.1161\/CIR.0000000000000527","article-title":"Update to practice standards for electrocardiographic monitoring in hospital settings: A scientific statement from the American Heart Association","volume":"136","author":"Sandau","year":"2017","journal-title":"Circulation"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24101","DOI":"10.1007\/s11042-023-16419-1","article-title":"A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images","volume":"83","author":"Sharma","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103046","DOI":"10.1016\/j.media.2023.103046","article-title":"Deep learning based synthesis of MRI, CT and PET: Review and analysis","volume":"92","author":"Dayarathna","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Counseller, Q., and Aboelkassem, Y. (2023). Recent technologies in cardiac imaging. Front. Med. Technol., 4.","DOI":"10.3389\/fmedt.2022.984492"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.jacc.2020.11.022","article-title":"2021 update to the 2017 ACC expert consensus decision pathway for optimization of heart failure treatment: Answers to 10 pivotal issues about heart failure with reduced ejection fraction: A report of the American College of Cardiology Solution Set Oversight Committee","volume":"77","author":"Maddox","year":"2021","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_17","first-page":"998","article-title":"In-hospital mortality and readmissions for heart failure in Spain. A Study of Index Episodes and 30-Day and 1-year Cardiac Readmissions","volume":"72","author":"Santos","year":"2019","journal-title":"Rev. Esp. Cardiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.ahj.2015.06.026","article-title":"A systematic review and meta-analysis on the association between quality of hospital care and readmission rates in patients with heart failure","volume":"170","author":"Fischer","year":"2015","journal-title":"Am. Heart J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hong, S., Zhou, Y., Shang, J., Xiao, C., and Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput. Biol. Med., 122.","DOI":"10.1016\/j.compbiomed.2020.103801"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: Data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, D., Lu, S., Zhang, L., and Liu, Y. (2023). Anomaly detection in chest X-rays based on dual-attention mechanism and multi-scale feature fusion. Symmetry, 15.","DOI":"10.3390\/sym15030668"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9256","DOI":"10.1109\/ACCESS.2017.2789324","article-title":"Predicting the risk of heart failure with EHR sequential data modeling","volume":"6","author":"Jin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4530","DOI":"10.1118\/1.3213085","article-title":"Patient-specific quality assurance method for VMAT treatment delivery","volume":"36","author":"Schreibmann","year":"2009","journal-title":"Med. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"D\u00e9sir, C., Bernard, S., Petitjean, C., and Heutte, L. (2012, January 1). A random forest based approach for one class classification in medical imaging. Proceedings of the Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France. Revised Selected Papers 3.","DOI":"10.1007\/978-3-642-35428-1_31"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"149595","DOI":"10.1109\/ACCESS.2019.2945527","article-title":"A new automatic identification method of heart failure using improved support vector machine based on duality optimization technique","volume":"7","author":"Geweid","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"48677","DOI":"10.1109\/ACCESS.2023.3276468","article-title":"Gradient Boosting Based Model for Elderly Heart Failure, Aortic Stenosis, and Dementia Classification","volume":"11","author":"Yongcharoenchaiyasit","year":"2023","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.31083\/j.rcm2204121","article-title":"Artificial intelligence in the diagnosis and detection of heart failure: The past, present, and future","volume":"22","author":"Yasmin","year":"2021","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100189","DOI":"10.1016\/j.teler.2025.100189","article-title":"ALZENET: Deep Learning-Based Early Prediction of Alzheimer\u2019s Disease through Magnetic Resonance Imaging Analysis","volume":"17","author":"Asaduzzaman","year":"2025","journal-title":"Telemat. Inform. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2550004","DOI":"10.1142\/S0219467825500044","article-title":"FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework","volume":"25","author":"Sahu","year":"2025","journal-title":"Int. J. Image Graph."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1007\/s00405-023-08299-w","article-title":"Machine learning for risk stratification of thyroid cancer patients: A 15-year cohort study","volume":"281","author":"Borzooei","year":"2024","journal-title":"Eur. Arch. Oto-Rhino-Laryngol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Du, W., Bi, W., Liu, Y., Zhu, Z., Tai, Y., and Luo, E. (2024). Machine learning-based decision support system for orthognathic diagnosis and treatment planning. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-04063-6"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pedreschi, D., Giannotti, F., Guidotti, R., Monreale, A., Ruggieri, S., and Turini, F. (February, January 27). Meaningful explanations of black box AI decision systems. Proceedings of the AAAI Conference on Artificial Intelligence 2019, Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.33019780"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12550","DOI":"10.1109\/JIOT.2020.3023105","article-title":"Automatic detection of congestive heart failure based on a hybrid deep learning algorithm in the internet of medical things","volume":"8","author":"Ning","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2020.04.056","article-title":"ECG heartbeat arrhythmia classification using time-series augmented signals and deep learning approach","volume":"171","author":"Kanani","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, S.H., Chuang, B.L., Lin, Y.H., Hung, C.S., and Ma, H.P. (2019, January 9\u201313). A Congestive Heart Failure Detection System via Multi-Input Deep Learning Networks. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Honolulu, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013460"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20313","DOI":"10.1109\/ACCESS.2020.2968900","article-title":"Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds","volume":"8","author":"Gjoreski","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nirschl, J., Janowczyk, A., Peyster, E., Frank, R., Margulies, K., Feldman, M., and Madabhushi, A. (2018). A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192726"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nirschl, J.J., Janowczyk, A., Peyster, E.G., Frank, R., Margulies, K.B., Feldman, M.D., and Madabhushi, A. (2017). Deep learning tissue segmentation in cardiac histopathology images. Deep Learning for Medical Image Analysis, Elsevier.","DOI":"10.1016\/B978-0-12-810408-8.00011-0"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhu, Y., Li, D., Yin, Y., and Zhang, J. (2020). Feature rearrangement based deep learning system for predicting heart failure mortality. Comput. Methods Programs Biomed., 191.","DOI":"10.1016\/j.cmpb.2020.105383"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1536\/ihj.19-714","article-title":"Diagnosing heart failure from chest X-ray images using deep learning","volume":"61","author":"Matsumoto","year":"2020","journal-title":"Int. Heart J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. (2017, January 21\u201326). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_45","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, X., Chen, Y., Bae, J., Li, H., Johnston, J., and Sanger, T. (2019, January 12\u201321). Predicting heart failure readmission from clinical notes using deep learning. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983095"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1001\/jamacardio.2021.6059","article-title":"High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning","volume":"7","author":"Duffy","year":"2022","journal-title":"JAMA Cardiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."},{"key":"ref_50","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_51","first-page":"457","article-title":"Smart heart monitoring: Early prediction of heart problems through predictive analysis of ECG signals","volume":"7","author":"Valehi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e059","DOI":"10.1093\/ehjopen\/oeae059","article-title":"Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction","volume":"4","author":"McCracken","year":"2024","journal-title":"Eur. Heart J."},{"key":"ref_53","first-page":"1","article-title":"Effects of exercise training on Fetuin-a in obese, type 2 diabetes and cardiovascular disease in adults and elderly: A systematic review and Meta-analysis","volume":"18","year":"2019","journal-title":"Lipids Health Dis."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"055011","DOI":"10.1088\/1361-6579\/ad46e2","article-title":"ELRL-MD: A deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration","volume":"45","author":"Kasmaee","year":"2024","journal-title":"Physiol. Meas."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e2443925","DOI":"10.1001\/jamanetworkopen.2024.43925","article-title":"Natural language processing of clinical documentation to assess functional status in patients with heart failure","volume":"7","author":"Adejumo","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_56","first-page":"75","article-title":"Automated identification of heart failure with reduced ejection fraction using deep learning-based natural language processing","volume":"13","author":"Nargesi","year":"2025","journal-title":"Heart Fail."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An introduction, MIT Press. [1st ed.].","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.bspc.2019.03.009","article-title":"Automatic staging model of heart failure based on deep learning","volume":"52","author":"Li","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_59","first-page":"45","article-title":"Computer-aided decision support system for symmetry-based prenatal congenital heart defects","volume":"2","author":"Sridevi","year":"2021","journal-title":"Adv. Mach. Vis. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1093\/eurheartj\/ehn360","article-title":"Differential effects of arginine methylation on diastolic dysfunction and disease progression in patients with chronic systolic heart failure","volume":"29","author":"Tang","year":"2008","journal-title":"Eur. Heart J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.eswa.2019.01.011","article-title":"Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning","volume":"122","author":"Ragab","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111359","DOI":"10.1016\/j.jss.2022.111359","article-title":"Data management for production quality deep learning models: Challenges and solutions","volume":"191","author":"Munappy","year":"2022","journal-title":"J. Syst. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Casella, B., Riviera, W., Aldinucci, M., and Menegaz, G. MiFL: Multi-Input Neural Networks in Federated Learning. Authorea Preprints, 2023.","DOI":"10.36227\/techrxiv.22492732.v1"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2160006","DOI":"10.1142\/S0218001421600065","article-title":"Enhancing the interpretability of deep models in healthcare through attention: Application to glucose forecasting for diabetic people","volume":"35","author":"Ammi","year":"2021","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1002\/biot.201100297","article-title":"Personalized health care: From theory to practice","volume":"7","author":"Snyderman","year":"2012","journal-title":"Biotechnol. J."},{"key":"ref_67","first-page":"9718","article-title":"AI-Driven Personalised Treatment Plans: The Future of Precision Medicine","volume":"17","author":"Chintala","year":"2023","journal-title":"Mach. Intell. Res."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Morrison, T., Pathmanathan, P., Adwan, M., and Margerrison, E. (2018). Advancing regulatory science with computational modeling for medical devices at the FDA\u2019s Office of Science and Engineering Laboratories. Front. Med., 5.","DOI":"10.3389\/fmed.2018.00241"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Krois, J., Garcia Cantu, A., Chaurasia, A., Patil, R., Chaudhari, P.K., Gaudin, R., Gehrung, S., and Schwendicke, F. (2021). Generalizability of deep learning models for dental image analysis. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-85454-5"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Chen, C., Bai, W., Davies, R., Bhuva, A., Manisty, C., Augusto, J., Moon, J., Aung, N., Lee, A., and Sanghvi, M. (2020). Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front. Cardiovasc. Med., 7.","DOI":"10.3389\/fcvm.2020.00105"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"228","DOI":"10.4258\/hir.2023.29.3.228","article-title":"Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive Explanations Approach","volume":"29","author":"Miranda","year":"2023","journal-title":"Healthc. Inform. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"48","DOI":"10.46604\/peti.2023.10101","article-title":"Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection","volume":"23","author":"Assegie","year":"2023","journal-title":"Proc. Eng. Technol. Innov."},{"key":"ref_73","unstructured":"Hedeker, D., and Gibbons, R. (2006). Longitudinal Data Analysis, John Wiley & Sons. [1st ed.]."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yacouby, R., and Axman, D. (2020, January 20). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, Online.","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"ref_75","unstructured":"Ratner, B. (2017). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Chapman and Hall\/CRC. [1st ed.]."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: The Framingham Heart Study","volume":"117","author":"Vasan","year":"2008","journal-title":"Circulation"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1093\/eurheartj\/ehs337","article-title":"Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies","volume":"34","author":"Pocock","year":"2013","journal-title":"Eur. Heart J."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","article-title":"Artificial intelligence in precision cardiovascular medicine","volume":"69","author":"Krittanawong","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_79","unstructured":"de Bakker, M. (2024). Circulating Biomarkers for Dynamic Cardiovascular Risk Assessment: A Precision Medicine Approach. [Ph.D. Thesis, Erasmus University Rotterdam]."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.jacr.2020.04.007","article-title":"Medical imaging and privacy in the era of artificial intelligence: Myth, fallacy, and the future","volume":"17","author":"Lotan","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kelly, C., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. 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