{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:25:46Z","timestamp":1793157946419,"version":"4.5.0"},"reference-count":84,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IVR Low Carbon Research Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. Methods: The research utilised quantitative 3D\/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project\u2019s (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging. The SCIR model, known for its robustness, was adapted with the Capuchin algorithm, adversarial debiasing, Fairlearn, and post-processing with equalised odds. The robustness of the SCIR model was further demonstrated in the fairness evaluation metrics, which included demographic parity, equal opportunity difference (0.037), equalised odds difference (0.026), disparate impact (1.081), and Theil Index (0.249). For interpretability, YOLOv5, Mask R-CNN, and ResNet18 were implemented with LIME and SHAP. Bias mitigation improved disparate impact (0.80 to 0.95), reduced equal opportunity difference (0.20 to 0.05), and decreased false favourable rates for males (0.0059 to 0.0033) and females (0.0096 to 0.0064) through balanced probability adjustment. Results: The SCIR model outperformed the SIR model (recovery rate: 1.38 vs 0.83) with a \u221210% transmission bias impact. Parameters (\u03b2=0.5, \u03b4=0.2, \u03b3=0.15) reduced susceptible counts to 2.53\u00d710\u221212 and increased recovered counts to 9.98 by t=50. YOLOv5 achieved high Intersection over Union (IoU) scores (94.8%, 93.7%, 80.6% for normal, severe, and abnormal cases). Mask R-CNN showed 82.5% peak confidence, while ResNet demonstrated a 10.4% accuracy drop under noise. Performance metrics (IoU: 0.91\u20130.96, Dice: 0.941\u20130.980, Kappa: 0.95) highlighted strong predictive accuracy and reliability. Conclusions: The findings validate the effectiveness of fairness-aware algorithms in addressing cardiovascular predictive model biases. The integration of fairness and explainable AI not only promotes equitable diagnostic precision but also significantly reduces diagnostic disparities across vulnerable populations. This reduction in disparities is a key outcome of the research, enhancing clinical trust in AI-driven systems. The promising results of this study pave the way for future work that will explore scalability in real-world clinical settings and address limitations such as computational complexity in large-scale data processing.<\/jats:p>","DOI":"10.3390\/diagnostics14232675","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:05:56Z","timestamp":1732701956000},"page":"2675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3503-6942","authenticated-orcid":false,"given":"Md Abu","family":"Sufian","sequence":"first","affiliation":[{"name":"IVR Low-Carbon Research Institute, Chang\u2019an University, Xi\u2019an 710018, China"},{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leichester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3019-9754","authenticated-orcid":false,"given":"Lujain","family":"Alsadder","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences Education, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wahiba","family":"Hamzi","sequence":"additional","affiliation":[{"name":"Laboratoire de Biotechnologie Sant\u00e9 et Environnement, Department of Biology, University of Blida, Blida 09000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadia","family":"Zaman","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences Education, Queen Mary University, London E1 4NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. S. M. Sharifuzzaman","family":"Sagar","sequence":"additional","affiliation":[{"name":"Department of AI and Robotics, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9446-2614","authenticated-orcid":false,"given":"Boumediene","family":"Hamzi","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA"},{"name":"The Alan Turing Institute, London NW1 2DB, UK"},{"name":"Department of Mathematics, Gulf University for Science and Technology, Mubarak Al-Abdullah 7207, Kuwait"},{"name":"Department of Mathematics, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Glorot","year":"2019","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1097\/00005053-198509000-00008","article-title":"Eye-Blink Rate in Tourette\u2019s Syndrome","volume":"173","author":"Karson","year":"1985","journal-title":"J. Nerv. Ment. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e069775","DOI":"10.1136\/bmj-2021-069775","article-title":"Racial bias and reproducibility in pulse oximetry among medical and surgical inpatients in general care in the Veterans Health Administration 2013-19: Multicenter, retrospective cohort study","volume":"378","author":"Valbuena","year":"2022","journal-title":"BMJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1097\/CCM.0000000000005394","article-title":"Disparities in Hypoxemia Detection by Pulse Oximetry Across Self-Identified Racial Groups and Associations With Clinical Outcomes","volume":"50","author":"Henry","year":"2022","journal-title":"Crit Care Med."},{"key":"ref_5","first-page":"3","article-title":"About healthy people","volume":"3","author":"People","year":"2018","journal-title":"Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Verma, S., and Rubin, J. (2018, January 28). Fairness definitions explained. Proceedings of the International Workshop on Software Fairness 2018, Gothenburg, Sweden.","DOI":"10.1145\/3194770.3194776"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s13643-022-01936-1","article-title":"Barriers and facilitators to access mental health services among refugee women in high-income countries: A systematic review","volume":"11","author":"DeSa","year":"2022","journal-title":"Syst. Rev."},{"key":"ref_8","unstructured":"American College of Cardiology (2024, September 21). Cover Story: One Size Does Not Fit All: Sex, Gender, Race, and Ethnicity in Cardiovascular Medicine. American College of Cardiology. Available online: https:\/\/www.acc.org\/latest-in-cardiology\/articles\/2018\/10\/14\/12\/42\/cover-story-one-size-does-not-fit-all-sex-gender-race-and-ethnicity-in-cardiovascular-medicine."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/s42256-021-00373-4","article-title":"Machine learning and algorithmic fairness in public and population health","volume":"3","author":"Mhasawade","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_10","unstructured":"Jones, G.P., Hickey, J.M., Di Stefano, P.G., Dhanjal, C., Stoddart, L.C., and Vasileiou, V. (2020). Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e32344","DOI":"10.2196\/32344","article-title":"Deep learning in mHealth for cardiovascular disease, diabetes, and cancer: Systematic review","volume":"10","author":"Triantafyllidis","year":"2022","journal-title":"JMIR mHealth uHealth"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yewale, D., and Vijayragavan, S.P. (2022). Comprehensive review on machine learning approach for heart disease prediction: Current status and future prospects. AIP Conference Proceedings, AIP Publishing.","DOI":"10.1063\/5.0080363"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.hfc.2021.11.003","article-title":"Machine learning in cardiovascular imaging","volume":"18","author":"Kagiyama","year":"2022","journal-title":"Heart Fail. Clin."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mooghali, M., Stroud, A.M., Yoo, D.W., Barry, B.A., Grimshaw, A.A., Ross, J.S., Barry, B.A., Grimshaw, A.A., Ross, J.S., and Zhu, X. (2024). Trustworthy and ethical AI-enabled cardiovascular care: A rapid review. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02653-6"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shuja, M., Qtaishat, A., Mishra, H.M., Kumar, M., and Ahmed, B. (2023, January 3\u20134). Machine Learning to Predict Cardiovascular Disease: Systematic Meta-Analysis. Proceedings of the 2023 6th International Conference on Information Systems and Computer Networks, Mathura, India.","DOI":"10.1109\/ISCON57294.2023.10112149"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Naser, M.A., Majeed, A.A., Alsabah, M., Al-Shaikhli, T.R., and Kaky, K.M. (2024). A Review of Machine Learning\u2019s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges. Algorithms, 17.","DOI":"10.3390\/a17020078"},{"key":"ref_17","unstructured":"Shishehbori, F., and Awan, Z. (2024). Enhancing Cardiovascular Disease Risk Prediction with Machine Learning Models. arXiv."},{"key":"ref_18","first-page":"1150933","article-title":"Cardiovascular diseases prediction by machine learning incorporation with deep learning","volume":"10","author":"Subramani","year":"2023","journal-title":"Frontiers"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dritsas, E., and Trigka, M. (2023). Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction. Sensors, 23.","DOI":"10.3390\/s23031161"},{"key":"ref_20","unstructured":"A3logics (2024, November 15). Adopting the New Emerging Trends in HR Tech. Available online: https:\/\/www.a3logics.com\/blog\/adopting-the-new-emerging-trends-in-hr-tech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s00127-015-1153-8","article-title":"The importance of post hoc approaches for overcoming non-response and attrition bias in population-sampled studies","volume":"51","author":"Gray","year":"2016","journal-title":"Soc. Psychiatry Psychiatr. Epidemiol."},{"key":"ref_22","unstructured":"Rutgers University (2024, November 18). Researchers Use Artificial Intelligence to Predict Cardiovascular Disease, ScienceDaily, Available online: https:\/\/www.sciencedaily.com\/releases\/2023\/02\/230227132723.htm."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s40537-023-00817-1","article-title":"Advanced machine learning techniques for cardiovascular disease early detection and diagnosis","volume":"10","author":"Baghdadi","year":"2023","journal-title":"J. Big Data"},{"key":"ref_24","first-page":"100100","article-title":"Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset","volume":"7","author":"Uddin","year":"2023","journal-title":"Intell.-Based Med."},{"key":"ref_25","unstructured":"Mark Melchionna (2024, October 22). AI Tools Can Support Early Cardiovascular Disease Diagnosis. Available online: https:\/\/www.techtarget.com\/healthtechanalytics\/news\/366590401\/AI-Tools-Can-Support-Early-Cardiovascular-Disease-Diagnosis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, F., Wu, P., Ong, H.H., Peterson, J.F., Wei, W.Q., and Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. J. Biomed. Inform., 138.","DOI":"10.1016\/j.jbi.2023.104294"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/14779072.2020.1732208","article-title":"Machine learning for predicting cardiac events: What does the future hold?","volume":"18","author":"Patel","year":"2020","journal-title":"Expert Rev. Cardiovasc. Ther."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10462-024-10899-9","article-title":"A comprehensive review of deep learning-based models for heart disease prediction","volume":"57","author":"Zhou","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10462-017-9552-8","article-title":"Machine learning based decision support systems (DSS) for heart disease diagnosis: A review","volume":"50","author":"Safdar","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Heckman, J.J. (1977). Sample Selection Bias as a Specification Error (with an Application to the Estimation of Labor Supply Functions), National Bureau of Economic Research. Available online: https:\/\/ideas.repec.org\/a\/ris\/apltrx\/0220.html.","DOI":"10.3386\/w0172"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1111\/cge.12948","article-title":"Facial dysmorphism is influenced by ethnic background of the patient and of the evaluator","volume":"92","author":"Lumaka","year":"2017","journal-title":"Clin. Genet."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"Gianfrancesco","year":"2018","journal-title":"JAMA Intern. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1080\/15265161.2023.2186516","article-title":"Preventing bias in medical devices: Identifying morally significant differences","volume":"23","author":"Jongsma","year":"2023","journal-title":"Am. J. Bioeth."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1136\/ebmed-2017-110883","article-title":"Catalogue of bias: Attrition bias","volume":"23","author":"Nunan","year":"2018","journal-title":"BMJ-Evid.-Based Med."},{"key":"ref_36","unstructured":"Bobbitt, Z. (2024, November 08). Cohen\u2019s Kappa Statistic: Definition & Example. Statology, Available online: https:\/\/www.statology.org\/cohens-kappa-statistic\/."},{"key":"ref_37","unstructured":"Rosebrock, A. (2024, November 08). Intersection over Union (IoU) for Object Detection. PyImageSearch, Available online: https:\/\/pyimagesearch.com\/2016\/11\/07\/intersection-over-union-iou-for-object-detection\/."},{"key":"ref_38","unstructured":"Shah, D. (2024, November 08). Intersection over Union (IoU): Definition, Calculation, Code. V7 Labs, Available online: https:\/\/www.v7labs.com\/blog\/intersection-over-union-guide."},{"key":"ref_39","unstructured":"Dickersin, K., and Min, Y.I. (1993). NIH clinical trials and publication bias. Online J. Curr. Clin. Trials, 50. Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/8306005\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Peddakrishna, S. (2023). Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes, 11.","DOI":"10.3390\/pr11041210"},{"key":"ref_41","unstructured":"Roy, P., Bera, P., Li, R., Shen, S., Zhang, X., Li, R., Wang, S., Zhou, B., and Wang, Z. (2019). Cardiovascular disease risk prediction based on random forest. Proceedings of the 2nd International Conference on Healthcare Science and Engineering, Springer. [2nd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, W., Fu, Y.-L., and Zhu, D. (2023). ResNet and its application to medical image processing: Research progress and challenges. Comput. Methods Programs Biomed., 240.","DOI":"10.1016\/j.cmpb.2023.107660"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mabrouk, A., Dahou, A., Elaziz, M.A., Redondo, R.P.D., and Kayed, M. (2023). Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things. arXiv.","DOI":"10.1155\/2022\/9112634"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.infoh.2024.06.001","article-title":"Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease","volume":"1","author":"Omotehinwa","year":"2024","journal-title":"Inform. Health"},{"key":"ref_45","unstructured":"Barocas, S., Hardt, M., and Narayanan, A. (2024, October 22). Fairness in Machine Learning. NIPS Tutorial. Available online: https:\/\/neurips.cc\/virtual\/2017\/tutorial\/8734."},{"key":"ref_46","unstructured":"Lipton, Z.C. (2016). The Mythos of Model Interpretability. arXiv."},{"key":"ref_47","unstructured":"Deng, S. (2022, January 21\u201323). Public opinion propagation model and simulation based on Impact model and improved SIR. Proceedings of the ICETIS 2022, 7th International Conference on Electronic Technology and Information Science, Sanya, China. Available online: https:\/\/ieeexplore.ieee.org\/document\/9788629."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vasudevan, V., Gnanasekaran, A., Sankar, V., Vasudevan, S.A., and Zou, J. (2021). Disparity in the quality of COVID-19 data reporting across India. BMC Public Health, 21.","DOI":"10.1186\/s12889-021-11054-7"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pias, T.S., Su, Y., Tang, X., Wang, H., Faghani, S., and Yao, D. (2023). Enhancing Fairness and Accuracy in Type 2 Diabetes Prediction through Data Resampling. medRxiv, 2023-05.","DOI":"10.1101\/2023.05.02.23289405"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1038\/s41591-023-02325-4","article-title":"Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations","volume":"29","author":"Doudesis","year":"2023","journal-title":"Nat. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TAI.2021.3083689","article-title":"Arrhythmic heartbeat classification using ensemble of random forest and support vector machine algorithm","volume":"2","author":"Bhattacharyya","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lu, J., Sattler, A., Wang, S., Khaki, A.R., Callahan, A., Fleming, S., Fong, R., Ehlert, B., Li, R.C., and Shieh, L. (2022). Considerations in the reliability and fairness audits of predictive models for advance care planning. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.943768"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"97879","DOI":"10.1109\/ACCESS.2023.3312046","article-title":"Enhancing prognosis accuracy for ischemic cardiovascular disease using K nearest neighbor algorithm: A robust approach","volume":"11","author":"Muhammad","year":"2023","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111183","DOI":"10.1016\/j.asoc.2023.111183","article-title":"A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare","volume":"152","author":"Chen","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e007988","DOI":"10.1161\/CIRCEP.119.007988","article-title":"Assessing and mitigating bias in medical artificial intelligence: The effects of ethnicity on a deep learning model for ECG analysis","volume":"13","author":"Noseworthy","year":"2020","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zu, W., Huang, X., Xu, T., Du, L., Wang, Y., Wang, L., and Nie, W. (2023). Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0287308"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1001\/jamasurg.2023.2293","article-title":"Development of a Machine Learning\u2013Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma","volume":"158","author":"Gebran","year":"2023","journal-title":"JAMA Surg."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","article-title":"ECG-based deep learning and clinical risk factors to predict atrial fibrillation","volume":"145","author":"Khurshid","year":"2022","journal-title":"Circulation"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s12170-024-00745-6","article-title":"Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity","volume":"18","author":"Amponsah","year":"2024","journal-title":"Curr. Cardiovasc. Risk. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.hfc.2020.05.002","article-title":"Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure","volume":"16","author":"Wehbe","year":"2020","journal-title":"Heart Fail Clin."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1097\/00005650-199506000-00005","article-title":"Variations in the utilization of coronary angiography for elderly patients with an acute myocardial infarction: An analysis using hierarchical logistic regression","volume":"33","author":"Gatsonis","year":"1995","journal-title":"Med. Care"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Puyol-Ant\u00f3n, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S.K., Neubauer, S., Petersen, S.E., Razavi, R., Chowienczyk, P., and King, A.P. (2022). Fairness in cardiac magnetic resonance imaging: Assessing sex and racial bias in deep learning-based segmentation. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.859310"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/j.cjca.2024.04.026","article-title":"Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models within Cardiology","volume":"40","author":"Lapalme","year":"2024","journal-title":"Can. J. Cardiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e635","DOI":"10.1016\/S2589-7500(20)30249-1","article-title":"Addressing bias: Artificial intelligence in cardiovascular medicine","volume":"2","author":"Tat","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Alaa, A.M., Bolton, T., Di Angelantonio, E., Rudd, J.H., and Van der Schaar, M. (2019). Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0213653"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e100460","DOI":"10.1136\/bmjhci-2021-100460","article-title":"Evaluating algorithmic fairness in the presence of clinical guidelines: The case of atherosclerotic cardiovascular disease risk estimation","volume":"29","author":"Foryciarz","year":"2022","journal-title":"BMJ Health Care Inform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e002395","DOI":"10.1136\/openhrt-2023-002395","article-title":"Algorithmic fairness in cardiovascular disease risk prediction: Overcoming inequalities","volume":"10","author":"Varga","year":"2023","journal-title":"Open Heart"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"101059","DOI":"10.1016\/j.patter.2024.101059","article-title":"FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare","volume":"5","author":"Liu","year":"2024","journal-title":"Patterns"},{"key":"ref_69","unstructured":"Skorupko, G., Osuala, R., Szafranowska, Z., Kushibar, K., Aung, N., Petersen, S.E., Lekadir, K., and Gkontra, P. (2024). Debiasing Cardiac Imaging with Controlled Latent Diffusion Models. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1093\/jamia\/ocae060","article-title":"Unmasking bias in artificial intelligence: A systematic review of bias detection and mitigation strategies in electronic health record-based models","volume":"31","author":"Chen","year":"2024","journal-title":"J. Am. Med. Informatics Assoc."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Chien, I., Deliu, N., Turner, R., Weller, A., Villar, S., and Kilbertus, N. (2022, January 21\u201324). Multi-disciplinary fairness considerations in machine learning for clinical trials. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea.","DOI":"10.1145\/3531146.3533154"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiao, Y., Wang, W.H., Ning, Y., Shenkman, E.A., Bian, J., and Wang, F. (2022). Algorithmic fairness in computational medicine. eBioMedicine, 84.","DOI":"10.1016\/j.ebiom.2022.104250"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1111\/insr.12402","article-title":"A review of multi-compartment infectious disease models","volume":"88","author":"Tang","year":"2020","journal-title":"Int. Stat. Rev."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ahj.2018.02.008","article-title":"The prognostic value of heart rate recovery in patients with coronary artery disease: A systematic review and meta-analysis","volume":"199","author":"Lachman","year":"2018","journal-title":"Am. Heart J."},{"key":"ref_76","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","author":"Powers","year":"2020","journal-title":"Int. J. Mach. Learn. Technol."},{"key":"ref_77","unstructured":"Li, E., Ai, F., and Liang, C. (2024, October 22). A Machine Learning model to Predict the Risk of Artificial Intelligence and Mental Health Care. Available online: https:\/\/www.google.co.uk\/books\/edition\/Artificial_Intelligence_and_Mental_Healt\/X-oYEQAAQBAJ?hl=en&gbpv=1."},{"key":"ref_78","unstructured":"Vereen, K. (2008). An Scir Model of Meningococcal Meningitis. [Master\u2019s Thesis, Virginia Commonwealth University]."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_80","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. Available online: http:\/\/www.cs.man.ac.uk\/~fumie\/tmp\/bishop.pdf."},{"key":"ref_81","unstructured":"Suresh, L., and Panigrahi, B. (2016). Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors. Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, Springer."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.image.2019.03.008","article-title":"Towards efficient medical lesion image super-resolution based on deep residual networks","volume":"75","author":"Ren","year":"2019","journal-title":"Signal Process. Image Commun."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Jinjri, W.M., Keikhosrokiani, P., and Abdullah, N.L. (2021, January 14\u201315). Machine learning algorithms for the classification of cardiovascular disease-A comparative study. Proceedings of the 2021 International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICIT52682.2021.9491677"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e13064","DOI":"10.1111\/exsy.13064","article-title":"Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques","volume":"39","author":"Theerthagiri","year":"2022","journal-title":"Expert Syst."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2675\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:40:49Z","timestamp":1760114449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":84,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232675"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232675","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]}}}