Prediction of Heart Disease Based on Data Mining Techniques
الموضوعات : International Journal of Data Envelopment Analysis
Alizadeh Mojtaba
1
,
Arash Moradi
2
1 - Department of Computer Engineering, Lorestan University, Khorramabad, Iran
2 - Department of Computer Engineering, Lorestan University, Khorramabad, Iran
الکلمات المفتاحية: Machine Learning, Heart Diseases, Data Mining,
ملخص المقالة :
Heart disease is one of the most popular health problems for human being. According to statistics published by the World Health Organization (WHO), two main diseases that caused about a quarter of deaths all around the world in 2016, were heart disease and stroke. Although a lot of articles suggested different ways of detection and treatment of the heart and its disease, no framework is proposed to leverage the machine learning techniques to do it. We tabulate 32 articles regarding the factors such as Algorithm, Database, Feature Selection, Evaluation Measures, Performance evaluation, Efficiency, and different used tools. The same factors also are leveraged for comparison including 17 papers that focus on ECG and HRV signals. The output of this comparison leads to a classification framework. All steps from pre-processing to classification are provided in the proposed framework. This survey and the proposed framework offer a roadmap that can help researchers to conduct future researches in the field. |
[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.