Subject Areas : International Journal of Data Envelopment Analysis
Alizadeh Mojtaba
1
,
Arash Moradi
2
1 -
2 -
Keywords:
Abstract :
References:
[1] (24 May 2018). The top 10 causes of death. Available: http://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
[2] S. Patidar, R. B. Pachori, and U. R. Acharya, "Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals," Knowledge-Based Systems, vol. 82, pp. 1-10, 2015.
[3] P. Libby and P. Theroux, "Pathophysiology of coronary artery disease," Circulation, vol. 111, pp. 3481-3488, 2005.
[4] S. Mendis, P. Puska, and B. Norrving, Global atlas on cardiovascular disease prevention and control: World Health Organization, 2011.
[5] I. Abubakar, T. Tillmann, and A. Banerjee, "Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013," Lancet, vol. 385, pp. 117-171, 2015.
[6] V. Khatibi and G. A. Montazer, "A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment," Expert Systems with Applications, vol. 37, pp. 8536-8542, 2010.
[7] P. W. Wilson, R. B. D’Agostino, D. Levy, A. M. Belanger, H. Silbershatz, and W. B. Kannel, "Prediction of coronary heart disease using risk factor categories," Circulation, vol. 97, pp. 1837-1847, 1998.
[8] S. Mittal, Coronary heart disease in clinical practice: Springer Science & Business Media, 2005.
[9] G. W. Barsness and D. R. Holmes, Coronary Artery Disease: New Approaches Without Traditional Revascularization: Springer Science & Business Media, 2011.
[10] M. Kivimäki, S. T. Nyberg, E. I. Fransson, K. Heikkilä, L. Alfredsson, A. Casini, et al., "Associations of job strain and lifestyle risk factors with risk of coronary artery disease: a meta-analysis of individual participant data," Canadian Medical Association Journal, vol. 185, pp. 763-769, 2013.
[11] J. Barth, M. Schumacher, and C. Herrmann-Lingen, "Depression as a risk factor for mortality in patients with coronary heart disease: a meta-analysis," Psychosomatic medicine, vol. 66, pp. 802-813, 2004.
[12] A. M. Roest, E. J. Martens, P. de Jonge, and J. Denollet, "Anxiety and risk of incident coronary heart disease: a meta-analysis," Journal of the American College of Cardiology, vol. 56, pp. 38-46, 2010.
[13] L. E. Cahill, A. Pan, S. E. Chiuve, Q. Sun, W. C. Willett, F. B. Hu, et al., "Fried-food consumption and risk of type 2 diabetes and coronary artery disease: a prospective study in 2 cohorts of US women and men–," The American journal of clinical nutrition, vol. 100, pp. 667-675, 2014.
[14] L. Dauchet, P. Amouyel, S. Hercberg, and J. Dallongeville, "Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies," The Journal of nutrition, vol. 136, pp. 2588-2593, 2006.
[15] D. Lloyd-Jones, R. Adams, M. Carnethon, G. De Simone, T. B. Ferguson, K. Flegal, et al., "Heart disease and stroke statistics—2009 update. A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee," Circulation, 2008.
[16] P. B. Jensen, L. J. Jensen, and S. Brunak, "Mining electronic health records: towards better research applications and clinical care," Nature Reviews Genetics, vol. 13, p. 395, 2012.
[17] J. Soni, U. Ansari, D. Sharma, and S. Soni, "Predictive data mining for medical diagnosis: An overview of heart disease prediction," International Journal of Computer Applications, vol. 17, pp. 43-48, 2011.
[18] R. A. Castellino, "Computer aided detection (CAD): an overview," Cancer Imaging, vol. 5, p. 17, 2005.
[19] R. M. Rangayyan and N. P. Reddy, "Biomedical signal analysis: a case-study approach," Annals of Biomedical Engineering, vol. 30, pp. 983-983, 2002.
[20] D. L. Hunt, R. B. Haynes, S. E. Hanna, and K. Smith, "Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review," Jama, vol. 280, pp. 1339-1346, 1998.
[21] D. Salas-Gonzalez, J. M. Górriz, J. Ramírez, M. López, I. Alvarez, F. Segovia, et al., "Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees," Physics in Medicine & Biology, vol. 55, p. 2807, 2010.
[22] R. Wu, W. Peters, and M. Morgan, "The next generation of clinical decision support: linking evidence to best practice," Journal of healthcare information management: JHIM, vol. 16, pp. 50-55, 2002.
[23] K. Kawamoto, C. A. Houlihan, E. A. Balas, and D. F. Lobach, "Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success," Bmj, vol. 330, p. 765, 2005.
[24] K. Singh and A. Wright, "Clinical decision support," in Clinical Informatics Study Guide, ed: Springer, 2016, pp. 111-133.
[25] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, "Advances in knowledge discovery and data mining," 1996.
[26] E. Turban, J. E. Aronson, T.-P. Liang, and R. Sharda, Decision Support and Business Intelligence Systems (8th Edition): Prentice-Hall, Inc., 2006.
[27] A. Sajadieh, V. Rasmussen, H. O. Hein, and J. F. Hansen, "Familial predisposition to premature heart attack and reduced heart rate variability," American Journal of Cardiology, vol. 92, pp. 234-236, 2003.
[28] J. M. Dekker, R. S. Crow, A. R. Folsom, P. J. Hannan, D. Liao, C. A. Swenne, et al., "Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study," Circulation, vol. 102, pp. 1239-1244, 2000.
[29] Z. Binici, M. R. Mouridsen, L. Køber, and A. Sajadieh, "Decreased nighttime heart rate variability is associated with increased stroke risk," Stroke, vol. 42, pp. 3196-3201, 2011.
[30] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," Circulation, vol. 101, pp. e215-e220, 2000.
[31] M. Kumar, R. B. Pachori, and U. R. Acharya, "Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals," Biomedical signal processing and control, vol. 31, pp. 301-308, 2017.
[32] U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, "Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network," Information sciences, vol. 405, pp. 81-90, 2017.
[33] I. Beraza and I. Romero, "Comparative study of algorithms for ECG segmentation," Biomedical Signal Processing and Control, vol. 34, pp. 166-173, 2017.
[34] Feature Extraction. Available: https://deepai.org/machine-learning-glossary-and-terms/feature-extraction
[35] M. L. Bermingham, R. Pong-Wong, A. Spiliopoulou, C. Hayward, I. Rudan, H. Campbell, et al., "Application of high-dimensional feature selection: evaluation for genomic prediction in man," Scientific reports, vol. 5, p. 10312, 2015.
[36] S. Karpagachelvi, M. Arthanari, and M. Sivakumar, "ECG feature extraction techniques-a survey approach," arXiv preprint arXiv:1005.0957, 2010.
[37] U. R. Acharya, H. Fujita, V. K. Sudarshan, S. L. Oh, M. Adam, J. E. Koh, et al., "Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads," Knowledge-Based Systems, vol. 99, pp. 146-156, 2016.
[38] U. R. Acharya, H. Fujita, M. Adam, O. S. Lih, V. K. Sudarshan, T. J. Hong, et al., "Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study," Information Sciences, vol. 377, pp. 17-29, 2017.
[39] E. W. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decision Support Systems, vol. 50, pp. 559-569, 2011.
[40] W. Wiharto, H. Kusnanto, and H. Herianto, "System Diagnosis of Coronary Heart Disease Using a Combination of Dimensional Reduction and Data Mining Techniques: A Review," Indonesian Journal of Electrical Engineering and Computer Science, vol. 7, pp. 514-523, 2017.
[41] G. Guidi, M. C. Pettenati, P. Melillo, and E. Iadanza, "A machine learning system to improve heart failure patient assistance," IEEE journal of biomedical and health informatics, vol. 18, pp. 1750-1756, 2014.
[42] O. W. Samuel, G. M. Asogbon, A. K. Sangaiah, P. Fang, and G. Li, "An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction," Expert Systems with Applications, vol. 68, pp. 163-172, 2017.
[43] M. P. McRae, B. Bozkurt, C. M. Ballantyne, X. Sanchez, N. Christodoulides, G. Simmons, et al., "Cardiac ScoreCard: a diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease," Expert Systems with Applications, vol. 54, pp. 136-147, 2016.
[44] S. Bashir, U. Qamar, and F. H. Khan, "BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting," Australasian physical & engineering sciences in medicine, vol. 38, pp. 305-323, 2015.
[45] A. D. Dolatabadi, S. E. Z. Khadem, and B. M. Asl, "Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM," Computer methods and programs in biomedicine, vol. 138, pp. 117-126, 2017.
[46] U. R. Acharya, V. K. Sudarshan, J. E. Koh, R. J. Martis, J. H. Tan, S. L. Oh, et al., "Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals," Biomedical Signal Processing and Control, vol. 31, pp. 31-43, 2017.
[47] Z. Masetic and A. Subasi, "Congestive heart failure detection using random forest classifier," Computer methods and programs in biomedicine, vol. 130, pp. 54-64, 2016.
[48] U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, "Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals," Information Sciences, vol. 415, pp. 190-198, 2017.
[1] (24 May 2018). The top 10 causes of death. Available: http://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
[2] S. Patidar, R. B. Pachori, and U. R. Acharya, "Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals," Knowledge-Based Systems, vol. 82, pp. 1-10, 2015.
[3] P. Libby and P. Theroux, "Pathophysiology of coronary artery disease," Circulation, vol. 111, pp. 3481-3488, 2005.
[4] S. Mendis, P. Puska, and B. Norrving, Global atlas on cardiovascular disease prevention and control: World Health Organization, 2011.
[5] I. Abubakar, T. Tillmann, and A. Banerjee, "Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013," Lancet, vol. 385, pp. 117-171, 2015.
[6] V. Khatibi and G. A. Montazer, "A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment," Expert Systems with Applications, vol. 37, pp. 8536-8542, 2010.
[7] P. W. Wilson, R. B. D’Agostino, D. Levy, A. M. Belanger, H. Silbershatz, and W. B. Kannel, "Prediction of coronary heart disease using risk factor categories," Circulation, vol. 97, pp. 1837-1847, 1998.
[8] S. Mittal, Coronary heart disease in clinical practice: Springer Science & Business Media, 2005.
[9] G. W. Barsness and D. R. Holmes, Coronary Artery Disease: New Approaches Without Traditional Revascularization: Springer Science & Business Media, 2011.
[10] M. Kivimäki, S. T. Nyberg, E. I. Fransson, K. Heikkilä, L. Alfredsson, A. Casini, et al., "Associations of job strain and lifestyle risk factors with risk of coronary artery disease: a meta-analysis of individual participant data," Canadian Medical Association Journal, vol. 185, pp. 763-769, 2013.
[11] J. Barth, M. Schumacher, and C. Herrmann-Lingen, "Depression as a risk factor for mortality in patients with coronary heart disease: a meta-analysis," Psychosomatic medicine, vol. 66, pp. 802-813, 2004.
[12] A. M. Roest, E. J. Martens, P. de Jonge, and J. Denollet, "Anxiety and risk of incident coronary heart disease: a meta-analysis," Journal of the American College of Cardiology, vol. 56, pp. 38-46, 2010.
[13] L. E. Cahill, A. Pan, S. E. Chiuve, Q. Sun, W. C. Willett, F. B. Hu, et al., "Fried-food consumption and risk of type 2 diabetes and coronary artery disease: a prospective study in 2 cohorts of US women and men–," The American journal of clinical nutrition, vol. 100, pp. 667-675, 2014.
[14] L. Dauchet, P. Amouyel, S. Hercberg, and J. Dallongeville, "Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies," The Journal of nutrition, vol. 136, pp. 2588-2593, 2006.
[15] D. Lloyd-Jones, R. Adams, M. Carnethon, G. De Simone, T. B. Ferguson, K. Flegal, et al., "Heart disease and stroke statistics—2009 update. A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee," Circulation, 2008.
[16] P. B. Jensen, L. J. Jensen, and S. Brunak, "Mining electronic health records: towards better research applications and clinical care," Nature Reviews Genetics, vol. 13, p. 395, 2012.
[17] J. Soni, U. Ansari, D. Sharma, and S. Soni, "Predictive data mining for medical diagnosis: An overview of heart disease prediction," International Journal of Computer Applications, vol. 17, pp. 43-48, 2011.
[18] R. A. Castellino, "Computer aided detection (CAD): an overview," Cancer Imaging, vol. 5, p. 17, 2005.
[19] R. M. Rangayyan and N. P. Reddy, "Biomedical signal analysis: a case-study approach," Annals of Biomedical Engineering, vol. 30, pp. 983-983, 2002.
[20] D. L. Hunt, R. B. Haynes, S. E. Hanna, and K. Smith, "Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review," Jama, vol. 280, pp. 1339-1346, 1998.
[21] D. Salas-Gonzalez, J. M. Górriz, J. Ramírez, M. López, I. Alvarez, F. Segovia, et al., "Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees," Physics in Medicine & Biology, vol. 55, p. 2807, 2010.
[22] R. Wu, W. Peters, and M. Morgan, "The next generation of clinical decision support: linking evidence to best practice," Journal of healthcare information management: JHIM, vol. 16, pp. 50-55, 2002.
[23] K. Kawamoto, C. A. Houlihan, E. A. Balas, and D. F. Lobach, "Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success," Bmj, vol. 330, p. 765, 2005.
[24] K. Singh and A. Wright, "Clinical decision support," in Clinical Informatics Study Guide, ed: Springer, 2016, pp. 111-133.
[25] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, "Advances in knowledge discovery and data mining," 1996.
[26] E. Turban, J. E. Aronson, T.-P. Liang, and R. Sharda, Decision Support and Business Intelligence Systems (8th Edition): Prentice-Hall, Inc., 2006.
[27] A. Sajadieh, V. Rasmussen, H. O. Hein, and J. F. Hansen, "Familial predisposition to premature heart attack and reduced heart rate variability," American Journal of Cardiology, vol. 92, pp. 234-236, 2003.
[28] J. M. Dekker, R. S. Crow, A. R. Folsom, P. J. Hannan, D. Liao, C. A. Swenne, et al., "Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study," Circulation, vol. 102, pp. 1239-1244, 2000.
[29] Z. Binici, M. R. Mouridsen, L. Køber, and A. Sajadieh, "Decreased nighttime heart rate variability is associated with increased stroke risk," Stroke, vol. 42, pp. 3196-3201, 2011.
[30] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," Circulation, vol. 101, pp. e215-e220, 2000.
[31] M. Kumar, R. B. Pachori, and U. R. Acharya, "Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals," Biomedical signal processing and control, vol. 31, pp. 301-308, 2017.
[32] U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, "Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network," Information sciences, vol. 405, pp. 81-90, 2017.
[33] I. Beraza and I. Romero, "Comparative study of algorithms for ECG segmentation," Biomedical Signal Processing and Control, vol. 34, pp. 166-173, 2017.
[34] Feature Extraction. Available: https://deepai.org/machine-learning-glossary-and-terms/feature-extraction
[35] M. L. Bermingham, R. Pong-Wong, A. Spiliopoulou, C. Hayward, I. Rudan, H. Campbell, et al., "Application of high-dimensional feature selection: evaluation for genomic prediction in man," Scientific reports, vol. 5, p. 10312, 2015.
[36] S. Karpagachelvi, M. Arthanari, and M. Sivakumar, "ECG feature extraction techniques-a survey approach," arXiv preprint arXiv:1005.0957, 2010.
[37] U. R. Acharya, H. Fujita, V. K. Sudarshan, S. L. Oh, M. Adam, J. E. Koh, et al., "Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads," Knowledge-Based Systems, vol. 99, pp. 146-156, 2016.
[38] U. R. Acharya, H. Fujita, M. Adam, O. S. Lih, V. K. Sudarshan, T. J. Hong, et al., "Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study," Information Sciences, vol. 377, pp. 17-29, 2017.
[39] E. W. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decision Support Systems, vol. 50, pp. 559-569, 2011.
[40] W. Wiharto, H. Kusnanto, and H. Herianto, "System Diagnosis of Coronary Heart Disease Using a Combination of Dimensional Reduction and Data Mining Techniques: A Review," Indonesian Journal of Electrical Engineering and Computer Science, vol. 7, pp. 514-523, 2017.
[41] G. Guidi, M. C. Pettenati, P. Melillo, and E. Iadanza, "A machine learning system to improve heart failure patient assistance," IEEE journal of biomedical and health informatics, vol. 18, pp. 1750-1756, 2014.
[42] O. W. Samuel, G. M. Asogbon, A. K. Sangaiah, P. Fang, and G. Li, "An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction," Expert Systems with Applications, vol. 68, pp. 163-172, 2017.
[43] M. P. McRae, B. Bozkurt, C. M. Ballantyne, X. Sanchez, N. Christodoulides, G. Simmons, et al., "Cardiac ScoreCard: a diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease," Expert Systems with Applications, vol. 54, pp. 136-147, 2016.
[44] S. Bashir, U. Qamar, and F. H. Khan, "BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting," Australasian physical & engineering sciences in medicine, vol. 38, pp. 305-323, 2015.
[45] A. D. Dolatabadi, S. E. Z. Khadem, and B. M. Asl, "Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM," Computer methods and programs in biomedicine, vol. 138, pp. 117-126, 2017.
[46] U. R. Acharya, V. K. Sudarshan, J. E. Koh, R. J. Martis, J. H. Tan, S. L. Oh, et al., "Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals," Biomedical Signal Processing and Control, vol. 31, pp. 31-43, 2017.
[47] Z. Masetic and A. Subasi, "Congestive heart failure detection using random forest classifier," Computer methods and programs in biomedicine, vol. 130, pp. 54-64, 2016.
[48] U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, "Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals," Information Sciences, vol. 415, pp. 190-198, 2017.
