Preliminary Experiment Results of Left Ventricular Remodelling Prediction Using Machine Learning Algorithms
Keywords:Machine Learning, Left Ventricular Remodeling, Magnetic Resonance Imaging, Classification.
AbstractLeft ventricular remodelling involves changes in the ventricular size, shape and function where abnormalities eventually lead to heart failure. Early prediction of left ventricular remodelling can help in enhancing clinical decision making in cardiac health management and reducing cardiovascular mortality. Although cardiac magnetic resonance imaging is increasingly being used in clinical assessment of cardiovascular diseases, there is scarce study on predicting the presence of left ventricular remodelling given the derived data from cardiac magnetic resonance images. Four parameters namely left ventricular end diastolic volume, left ventricular end systolic volume, ejection fraction and occurrence/absence of oedema are used for prediction. A preliminary experiment is conducted where multi-layer perceptron and support vector machine are trained with the parameters obtained from cardiac magnetic resonance images in predicting between patients with left ventricular remodelling or normal. The preliminary experimental results indicated that support vector machine model performed better than multi-layer perception.
Kementerian Kesihatan Malaysia (KKM) Health Facts; http://www.moh.gov.my/index.php/pages/view/58.
V. Rozzelo, D. Poldemans, and E. Biagini, “Prognosis of patients with ischaemic cardiomyopathy after coronary revascularization in relation to viability and improvement if left ventricular ejection fraction,” Heart, 95, pp. 1273-1277, 2009.
R. Bonow, G. Maurer, and K. Lee, “Myocardial viability and survival in ischaemic left ventricular dysfunction,” The New England Journal of Medicine, 364(17), pp. 1617-1625. DOI: 10.1056/NEJMoa1100358
S.D. Pokorney, J.F. Rodriguez, J.T. Ortiz, et al. “Infarct healing is a dynamic process following acute myocardial infarction,” Journal of Cardiovascular Magnetic Resonance, 14:62, 2012. DOI: 10.1186/1532-429X-14-62.
R. Nijveldt, A.M. Beek, A. Hirsch, M.G. Stoel, M.B. Hofman, V.A. Umans, P.R. Algra, J.W. Twisk and A.C. van Rossum, “Functional recovery after acute myocardial infarction: comparison between angiography, electrocardiography, and cardiovascular magnetic resonance measures of microvascular injury,” Journal of the American College of Cardiology, 52, pp. 181–189, 2008.
V. Bodi, J. Sanchis, M.P. Lopez-Lereu, A. Losada, J. Nunez, M. Pellicer, V. Bertomeu, F.J. Chorro and A. Llacer, “Usefulness of a comprehensive cardiovascular magnetic resonance imaging assessment for predicting recovery of left ventricular wall motion in the setting of myocardial stunning,” Journal of the American College of Cardiology, 46, pp. 1747–1752, 2005.
S. Ardekani, R.G.Weiss, A.C. Lardo, R.T. George, J.A.C. Lima, K.C. Wu, M.I. Miller, R.L. Winslow and L. Younes, “Computational Method for Identifying and Quantifying Shape Features of Human Left Ventricular Remodeling,” Annals of Biomedical Engineering, 37(6), pp. 1043-1054, 2009. DOI: 10.1007/s10439-009-9677-2.
T. Yang, Y.A. Chiao, Y. Wang, A. Voorhess, H. Han, M.L. Lindsey and Y. Jin, “Mathematical modelling of left ventricular dimensional changes in mice during aging,” BMC Systems Biology, 6 (Supplement 3): S10, 2012. DOI: 10.1186/1752-0509-6-S3-S10.
Y. Devaux, M. Vausort, G.P. McCann, J. Zangrando, D. Kelly, N. Razvi, L. Zhang, L.L. Ng, D.R. Wagner and I.B. Squire, “MicroRNA-10: A novel marker of left ventricular remodeling after acute myocardial infarction,” Circulation: Cardiovascular Genetics, 6, pp. 290-298, 2013.
R.C. Deo, “Machine learning in Medicine,” Circulation, 132(20), pp. 1920-1930, 2015. DOI: 10.1161/CIRCULATIONAHA.115.001593.
S.B. Kotsiantis, “Supervised machine learning: a review of classification techniques,” Informatica, pp.249-268, 2007.
E.M. Sweeney, J.T. Vogelstein, J.L. Cuzzocreo, P.A. Calabresi, D.S. Reich, C.M. Crainiceanu,R.T. Shinohara, “A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI,” PLoS ONE, 9(4): e95753, 2014. DOI:10.1371/journal.pone.0095753.
C.F. Moreno-Garcia, M. Aceves-Martins and F. Serratosa, “Unsupervised machine learning application to perform a systematic review and meta-analysis in medical research,” Computacion Sistemas, 20(1), pp.7-17, 2016. DOI: 10.13053/CyS-20-1-2360.
R. Gentleman and V.J. Carey, “Unsupervised machine learning,” Bioconductor Case Studies. New York: Springer, pp. 137 -157, 2008. DOI: 10.1007/978-0-387-77240-0_10.
F. Amato, A. López, E.M. Peña-Méndez, P. Vaňhara, A. Hampl and J. Havel, “Artificial neural networks in medical diagnosis,” Journal of Applied Biomedicine, 11, pp. 47–58, 2013.
R. Raut and S.V. Dudul, “Intelligent diagnosis of heart diseases using neural network approach,” International Journal of Computer Application (0975-887), 1 (2), pp. 97-102, 2010.
E. Choi, A. Schuetz, W.F. Stewart and J. Sun, “Using recurrent neural network models for early detection of heart failure onset,” Journal of the American Medical Informatics Association, 24(2), pp.361-370, 2016. DOI: 10.1093/jamia/ocw112
J.H. Cole, R.P.K. Poudel, D. Tsagkrasoulis, M.W.A. Caan, C. Steves, T.D. Spector and G. Montana, “Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker,” (preprint) arXiv:1612.02572v1 [stat.ML], 2016.
H. Yan, Y. Jiang, J. Zheng, C. Peng and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert System with Application, 30, pp.272 -281, 2006. DOI: 10.1016/j.eswa.2005.07.022
J. Kojuri, R. Boostani, P. Dehghani, F. Nowroozipour and N. Saki, “Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram,” Journal of Cardiovascular Disease Research, 6(2), pp. 51-59, 2015.
R.A. Collazo, L.A.M. Pessˆoa, L. Bahiense, B. de Braganc¸a Pereira, A. F. dos Reis and N.S. e Silva, “A comparative study between artificial neural network and support vector machine for acute coronary syndrome prognosis,” Pesquisa Operacional, 36(2), pp. 321-343, 2016. DOI: 10.1590/0101-7438.2016.036.02.0321.
G.B. Berikol, O. Yildiz, and İ.T Özcan, “Diagnosis of acute coronary syndrome with a support vector machine,” Journal of Medical System, 40(4): 84, 2016. DOL:10.1007/s10916 -016-0432-6.
D. N. F. Awang Iskandar and H. Ujir, “Spatio-temporal semantic representation of Cardiac MRI in heart attack patients,” 9th International Conference on IT in Asia (CITA), pp. 1-5, 2015. DOI: 10.1109/CITA.2015.7349841
P. Radau, Y. Lu, K. Connelly, G. Paul, A.J. Dick and G.A. Wright, “Evaluation framework for algorithms segmenting short axis cardiac MRI,” The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge 2009, http://hdl.handle.net/10380/3070
E. Larose, J. Rodés-Cabau, P. Pibarot P, S. Rinfret, G. Proulx, C.M. Nguyen, J. Déry, O. Gleeton, L. Roy, b. Noél, G. Barbeau, J. Rouleau, J. Boudreault, M. Amyot and R. De Larochelliére, “Predicting late myocardial recovery and outcomes in the early hours of ST-segment elevation myocardial infarction: traditional measures compared with microvascular obstruction, salvaged myocardium, and necrosis characteristics by cardiovascular magnetic resonance,” Journal of the American College of Cardiolog, 55(22), pp.2459–2469, 2010. DOI: 10.1016/j.jacc.2010.02.033
A. Khan, D.N.F. Awang Iskandar, H. Ujir and Y.C. Wang, “Automatic segmentation of CMRIs for LV contour detection,” In: H. Ibrahim, S. Iqbal, S.S. Teoh and M.T. Mustaffa (eds) 9 th International Conference on Robotic, Vision, Signal Processing and Power Applications (RoViSP 2016), Lecture Notes in Electrical Engineering, 398, pp. 313-319, Springer Singapore. DOI: 10.1007/978-981-10-1721-6_34.
K. Alfakih, S. Plein, H. Thiele, T. Jones, J.P. Ridgway and M.U. Sivananthan, “Normal human left and right ventricular dimensions for MRI as assessed by turbo gradient echo and steady -state free precession imaging sequences,” Journal of Magnetic Resonance Imaging, 17(3), pp.323 -329, 2003. DOI: 10.1002/jmri.10262
MATLAB® 7.5 and Neural Network Toolbox™, The MathWorks, Inc., Natick, Massachusetts, United States.
R. Kohavi and F. Provost, “On Applied Research in Machine Learning,” In Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, Columbia University, New York, 30, 1998.
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