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Global Translational Medicine                                       Evaluating ML models for CAD prediction



               intelligence in healthcare. Future Healthc J. 2019;6(2):94-98.     doi: 10.1016/S0140-6736(22)02079-7

               doi: 10.7861/futurehosp.6-2-94                  23.  Cho SY, Kim SH, Kang SH, et al. Pre-existing and machine
                                                                  learning-based models for cardiovascular risk prediction.
            14.  Javaid M, Haleem A, Pratap Singh R, Suman R, Rab S.   Sci Rep. 2021;11(1):8886.
               Significance of machine learning in healthcare: Features,
               pillars and applications. Int J Intell Netw. 2022;3:58-73.     doi: 10.1038/s41598-021-88257-w
               doi: 10.1016/j.ijin.2022.05.002                 24.  Lee YH, Tsai TH, Chen JH,  et al. Machine learning of
                                                                  treadmill exercise test to improve selection for testing for
            15.  Srinivasan S, Gunasekaran S, Mathivanan SK, Malar MB,   coronary artery disease. Atherosclerosis. 2022;340:23-27.
               Jayagopal P, Dalu GT. An active learning machine technique
               based prediction of cardiovascular heart disease from UCI-     doi: 10.1016/j.atherosclerosis.2021.11.028
               repository database. Sci Rep. 2023;13:13588.    25.  Özbilgin F, Kurnaz Ç, Aydın, E. Prediction of coronary
               doi: 10.1038/s41598-023-40717-1                    artery disease using machine learning techniques with iris
                                                                  analysis. Diagnostics. 2023;13(6):1081.
            16.  Garavand A, Behmanesh A, Aslani N, Sadeghsalehi H,
               Ghaderzadeh M. Towards diagnostic aided systems in      doi: 10.3390/diagnostics13061081
               coronary artery disease detection: A  comprehensive   26.  Sun Z, Silberstein J, Vaccarezza M. Cardiovascular
               multiview survey of the state of the art.  Int J Intell Syst.   computed tomography in the diagnosis of cardiovascular
               2023;2023:6442756.                                 disease: Beyond lumen assessment.  J  Cardiovasc Dev Dis.
                                                                  2024;11(1):22.
               doi: 10.1155/2023/6442756
                                                                  doi: 10.3390/jcdd11010022
            17.  Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T,
               Budoff M, Naghavi M. Machine learning outperforms ACC/  27.  Ahsan MM, Siddique Z. Machine learning-based heart
               AHA CVD risk calculator in MESA.  J  Am Heart Assoc.   disease diagnosis: A systematic literature review. Artif Intell
               2018;7(22):e009476.                                Med. 2022;128:102289.
               doi: 10.1161/JAHA.118.009476                       doi: 10.1016/j.artmed.2022.102289
            18.  Baskaran L, Ying X, Xu Z, et al. Machine learning insight into   28.  Janosi A, Steinbrunn W, Pfisterer M, Detrano R.  Heart
               the role of imaging and clinical variables for the prediction   Disease. UCI Machine Learning Repositoryl; 1988.
               of obstructive coronary artery disease and revascularization:      doi: 10.24432/C52P4X
               An exploratory analysis of the CONSERVE study. PLoS One.
               2020;15:e0233791.                               29.  Unknown. (n.d.). Statlog (Heart) [Dataset]. UCI Machine
                                                                  Learning Repository.
               doi: 10.1371/journal.pone.0233791
                                                                  doi: 10.24432/C57303
            19.  Al’Aref SJ, Maliakal G, Singh G, et al. Machine learning of
               clinical variables and coronary artery calcium scoring for   30.  Hicks SA, Strümke I, Thambawita V, et al. On evaluation
               the prediction of obstructive coronary artery disease on   metrics for medical applications of artificial intelligence. Sci
                                                                  Rep. 2022;12(1):5979.
               coronary computed tomography angiography: Analysis
               from the CONFIRM registry. Eur Heart J. 2020;41:359-367.     doi: 10.1038/s41598-022-09954-8
               doi: 10.1093/eurheartj/ehz565                   31.  Delgado R, Tibau XA. Why Cohen’s Kappa should be
                                                                  avoided as performance measure in classification. PLoS One.
            20.  Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der   2019;14(9):e0222916.
               Schaar M. Cardiovascular disease risk prediction using
               automated machine learning: A prospective study of 423,604      doi: 10.1371/journal.pone.0222916
               UK Biobank participants. PLoS One. 2019;14(5):e0213653.  32.  Chicco D, Jurman G. The Matthews correlation coefficient
               doi: 10.1371/journal.pone.0213653                  (MCC) should replace the ROC AUC as the standard
                                                                  metric for assessing binary classification.  BioData Min.
            21.  Motwani M, Dey D, Berman DS, et al. Machine learning for   2023;16(1):4.
               prediction of all-cause mortality in patients with suspected
               coronary artery disease: A  5-year multicentre  prospective      doi: 10.1186/s13040-023-00322-4
               registry analysis. Eur Heart J. 2017;38(7):500-507.  33.  McHugh ML. Interrater reliability: The kappa statistic.
               doi: 10.1093/eurheartj/ehw188                      Biochem Med (Zagreb). 2012;22(3):276-282.
            22.  Forrest IS, Petrazzini BO, Duffy Á, et al. Machine learning-  34.  Xia Y. Correlation and association analyses in microbiome
               based marker  for  coronary artery disease: Derivation   study integrating multiomics in health and disease. Prog Mol
               and validation in two longitudinal cohorts.  Lancet.   Biol Transl Sci. 2020;171:309-491.
               2023;401(10372):215-225.                           doi: 10.1016/bs.pmbts.2020.04.003


            Volume 3 Issue 1 (2024)                         12                       https://doi.org/10.36922/gtm.2669
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