References
- Lauer MS, Anderson KM, Kannel WB, et al. The impact of obesity on left ventricular mass and geometry: the Framingham Heart Study. JAMA. 1991;266(2):231–236.
- Amarbayasgalan T, Pham V-H, Theera-Umpon N, et al. An efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. IEEE Access. 2021;9:135210–135223.
- Hammond IW, Devereux RB, Alderman MH, et al. Relation of blood pressure and body build to left ventricular mass in normotensive and hypertensive employed adults. J Am Coll Cardiol. 1988;12(4):996–1004.
- Lauer MS, Anderson KM, Levy D. Separate and joint influences of obesity and mild hypertension on left ventricular mass and geometry: the Framingham Heart Study. J Am Coll Cardiol. 1992;19(1):130–134.
- Levy D, Garrison RJ, Savage DD, et al. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. N Engl J Med. 1990;322(22):1561–1566.
- Alexander JK, Dennis EW, Smith WG, et al. Blood volume, cardiac output, and distribution of systemic blood flow in extreme obesity. Cardiovasc Res Cent Bull. 1962;1:39.
- Kim H, Ishag M, Piao M, et al. A data mining approach for cardiovascular disease diagnosis using heart rate variability and images of carotid arteries. Symmetry (Basel). 2016;8(6):47. DOI:10.3390/sym8060047
- Soni J, Ansari U, Sharma D, et al. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int J Comput Appl. 2011;17(8):43–48.
- Van der Geest RJ, Buller VG, Jansen E, et al. Comparison between manual and semiautomated analysis of left ventricular volume parameters from short-axis MR images. J Comput Assisted Tomogr. 1997;21(5):756–765.
- Nesser H-J, Mor-Avi V, Gorissen W, et al. Quantification of left ventricular volumes using three-dimensional echocardiographic speckle tracking: comparison with MRI. Eur Heart J. 2009;30(13):1565–1573.
- Cury RC, Shash K, Nagurney JT, et al. Cardiac magnetic resonance with T2-weighted imaging improves detection of patients with acute coronary syndrome in the emergency department. Circulation. 2008;118(8):837–844.
- Hunold P, Schlosser T, Vogt FM, et al. Myocardial late enhancement in contrast-enhanced cardiac MRI: Distinction between infarction scar and non–infarction-related disease. Am J Roentgenol. 2005;184(5):1420–1426.
- Bosch JG, Mitchell SC, Lelieveldt BP, et al. Automatic segmentation of echocardiographic sequences by active appearance motion models. Medical Imaging. IEEE Trans. 2002;21(11):1374–1383.
- Petersen SE, Selvanayagam JB, Wiesmann F, et al. Left ventricular non-compaction: insights from cardiovascular magnetic resonance imaging. J Am Coll Cardiol. 2005;46(1):101–105.
- Mannil M, von Spiczak J, Manka R, et al. Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible. Invest Radiol. 2018;53(6):338–343.
- Braun T, Spiliopoulos S, Veltman C, et al. Detection of myocardial ischemia due to clinically asymptomatic coronary artery stenosis at rest using supervised artificial intelligence-enabled vectorcardiography – a five-fold cross validation of accuracy. J Electrocardiol. 2020;59:100–105.
- Gonsalves AH, Thabtah F, Mohammad RMA, et al. Prediction of coronary heart disease using machine learning: An experimental analysis. Proc. 3rd Int. Conf. Deep Learn. Technol. (ICDLT). 2019: 51–56.
- Beunza JJ, Puertas E, García-Ovejero E, et al. Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J Biomed Informat. 2019;97:Art. no. 103257. Accessed July 9, 2021. DOI:10.1016/j.jbi.2019.103257
- Kim J, Lee J, Lee Y. Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare Informat Res. 2015;21(3):167–174.
- Lim K, Lee BM, Kang U, et al. An optimized DBN-based coronary heart disease risk prediction. Int J Comput Commun Control. 2018;13(4):492–502. DOI:10.15837/ijccc.2018.4.3269
- Luo J, He F, Gao X. An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models. Integr Comput-Aided Eng. 2022;30(1):89–104..
- Chen Y, He F, Li H, et al. A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration. Appl Soft Comput. 2020;93:106335.
- Li H, He F, Chen Y, et al. MLFS-CCDE: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution. Memetic Comput. 2021;13(1):1–18.
- Liang Y, He F, Zeng X, et al. An improved loop subdivision to coordinate the smoothness and the number of faces via multi-objective optimization. Integr Comput-Aided Eng. 2022;29(1):23–41..
- Li C, Xu C, Gui C, et al. Level set evolution without re-initialization: a new variational formulation. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on 2005 Jun 20 (Vol. 1, pp. 430–436).
- Shape Analysis & Measurement, CIS 6320, Lecture Notes, University of Guelph Canada. 2001.
- Nasien D, Haron H, Yuhaniz SS. The heuristic extraction algorithms for freeman chain code of handwritten character. Int J Exp Algorithms. 2011;1(1):1–20.
- Kekre HB, Thepade SD, Maloo A. Eigenvectors of covariance matrix using row mean and column mean sequences for face recognition. Int J Biometrics Bioinf. 2010;4(2):42–50.
- Turk M, Pentland A. Eigenfaces for recognition. J Cognit Neurosci. 1991;3(1):71–86.
- Cheadle C, Cho-Chung YS, Becker KG, et al. Application of z-score transformation to Affymetrix data. Appl Bioinf. 2003;2(4):209–217.
- De Falco I, Della Cioppa A, Tarantino E. Facing classification problems with particle swarm optimization. Elsevier Appl Soft Comput. 2007;7:652–658.
- Jiji GW, Johnson DuraiRaj P. Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique. Appl Soft Comput. 2015;30:650–662.