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AIMS Medical Science

2016, Volume 3, Issue 2: 217-233. doi: 10.3934/medsci.2016.2.217
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Research article

A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression

  • Centre for Research in Complex Systems and School of Community Health, Charles Sturt University, Albury, NSW, Australia
  • Received: 29 May 2016 Accepted: 08 July 2016 Published: 12 July 2016

  • Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.

    Citation: Herbert F. Jelinek, Andrei V. Kelarev. A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression[J]. AIMS Medical Science, 2016, 3(2): 217-233. doi: 10.3934/medsci.2016.2.217

    Related Papers:

  • Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.


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    [1] Fernandez-Llatas C, García-Gómez JM (2014) Data mining in clinical medicine. New York: Springer.
    [2] Cerrito P (2010) Cases on health outcomes and clinical data mining: studies and frameworks. Hershey, Pennsylvania, USA: IGI Global.
    [3] Epstein I (2010) Clinical data-mining: integrating practice and research. Oxford, UK: Oxford University Press.
    [4] Petillo D, Orey S, Tan AC, et al. (2014) Parkinson’s disease-related circulating microRNA biomarkers—a validation study. AIMS Med Sci 2: 7-14.
    [5] DeMarshall CA, Sarkar A, Nagele RG (2015) Serum autoantibodies as biomarkers for Parkinson’s disease: background and utility. AIMS Med Sci 2: 316-327. doi: 10.3934/medsci.2015.4.316
    [6] Ervin K, Pallant J, Terry DR, et al. (2015) A descriptive study of health, lifestyle and sociodemographic characteristics and their relationship to known dementia risk factors in rural Victorian communities. AIMS Med Sci 2: 246-260. doi: 10.3934/medsci.2015.3.246
    [7] Shinde S, Mukhopadhyay S, Mohsen G, et al. (2015) Biofluid-based microRNA biomarkers for Parkinson’s disease: an overview and update. AIMS Med Sci 2: 15-25. doi: 10.3934/medsci.2015.1.15
    [8] White VJ, Nayak RC (2015) Re-circulating phagocytes loaded with CNS debris: a potential marker of neurodegeneration in Parkinsons disease? AIMS Med Sci 2: 26-34. doi: 10.3934/medsci.2015.1.26
    [9] Khalid KE, Nsairat HN, Zhang JZ (2016) The presence of interleukin 18 binding protein isoforms in Chinese patients with rheumatoid arthritis. AIMS Med Sci 3: 103-113. doi: 10.3934/medsci.2016.1.103
    [10] Fitzmaurice MJ, Adams K, Eisenberg JM (2002) Three decades of research on computer applications in health care: medical informatics support at the agency for healthcare research and quality. JAMIA 9:144-160.
    [11] Hage I, Hamade R (2015) Automatic detection of cortical bone’s Haversian osteonal boundaries. AIMS Med Sci 2: 328-346. doi: 10.3934/medsci.2015.4.328
    [12] Vinik AI, Erbas T, Casellini CM (2013) Diabetic cardiac autonomic neuropathy, inflammation and cardiovascular disease. J Diabetes Investig 4: 4-18. doi: 10.1111/jdi.12042
    [13] Jelinek HF, Abawajy JH, Kelarev AV, et al. (2014) Decision trees and multi-level ensemble classifiers for neurological diagnostics. AIMS Med Sci 1: 1-12.
    [14] Jelinek HF, Abawajy JH, Cornforth D, et al. (2015) Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes. AIMS Med Sci 2: 396-409. doi: 10.3934/medsci.2015.4.396
    [15] Deshpande AD, Harris-Hayes M, Schootman M (2008) Epidemiology of diabetes and diabetes-related complications. Phys Ther 88: 1254-1264. doi: 10.2522/ptj.20080020
    [16] Colagiuri S, Colagiuri R, Ward J (1998) National diabetes strategy and implementation plan. Canberra: Paragon Printers.
    [17] Pop-Busui R, Evans GW, Gerstein HC, et al. (2010) The ACCORD Study Group. Effects of cardiac autonomic dysfunction on mortality risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial. Diab Care 33: 1578-1584.
    [18] Spallone V, Ziegler D, Freeman R, et al. (2011) Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management. Diab Metab Res Rev 27: 639-653.
    [19] Ziegler D. (1994) Diabetic cardiovascular autonomic neuropathy: prognosis, diagnosis and treatment. Diab Metabol Rev 10: 339-383.
    [20] Gerritsen J, Dekker JM, TenVoorde BJ, et al. (2001) Impaired autonomic function is associated with increased mortality, especially in subjects with diabetes, hypertension or a history of cardiovascular disease. Diab Care 24: 1793-1798. doi: 10.2337/diacare.24.10.1793
    [21] Pappachan JM, Sebastian J, Bino BC, et al. (2008) Cardiac autonomic neuropathy in diabetes mellitus: prevalence, risk factors and utility of corrected QT interval in the ECG for its diagnosis. Postgrad Med J 84: 205-210. doi: 10.1136/pgmj.2007.064048
    [22] Zoppini G, Cacciatori V, Raimondo D, et al. (2015) Prevalence of cardiovascular autonomic neuropathy in a cohort of patients with newly diagnosed type 2 diabetes: the Verona newly diagnosed type 2 diabetes study (VNDS). Diabetes Care 38: 1487-1493. doi: 10.2337/dc15-0081
    [23] Jelinek HF, Imam HM, Al-Aubaidy H, et al. (2013) Association of cardiovascular risk using nonlinear heart rate variability measures with the Framingham risk score in a rural population. Frontiers Physiol 4: 1-8.
    [24] Tesfaye N, Seaquist ER (2010) Neuroendocrine responses to hypoglycemia. Ann NY Acad Sci 1212: 12-28. doi: 10.1111/j.1749-6632.2010.05820.x
    [25] Hoeldtke RD, Boden G (1994) Epinephrine secretion, hypoglycemia unawareness, and diabetic autonomic neuropathy. Ann Intern Med 120: 512-517. doi: 10.7326/0003-4819-120-6-199403150-00011
    [26] Balcıoğlu AS, Müderrisoğlu H (2015) Diabetes and cardiac autonomic neuropathy: Clinical manifestations, cardiovascular consequences, diagnosis and treatment. World J Diab 6: 80-91. doi: 10.4239/wjd.v6.i1.80
    [27] John SC, Easton JD (2003) Are patients with acutely recovered cerebral ischemia more unstable? Stroke 4: 24-46.
    [28] Ko SH, Kwon HS, Lee JM, et al. (2006) Cardiovascular autonomic neuropathy in patients with type 2 diabetes mellitus. J Korean Diab Assoc 30: 226-235.
    [29] Stranieri A, Abawajy J, Kelarev A, et al. (2013) An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy. Artif Intell Med 58: 185-193. doi: 10.1016/j.artmed.2013.04.007
    [30] Devoe JE, Gold R, McIntire P, et al., (2011) Electronic health records vs Medicaid claims: completeness of diabetes preventive care data in community health centers. Ann Fam Med 9: 351-358. doi: 10.1370/afm.1279
    [31] Bellazzi R, Ferrazzi F, Sacchi L (2011) Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley Int Rev Data Mining Know Discover1: 416-430.
    [32] Habibi S, Ahmadi M, Alizadeh S, (2015) Type 2 diabetes mellitus screening and risk factors using decision tree: results of data mining. Glob J Health Sci 7: 304-310.
    [33] Ali R, Hussain J, Siddiqi MH, et al. (2015) H2RM: a hybrid rough set reasoning model for prediction and management of diabetes mellitus. Sensors (Basel) 15: 15921-15951. doi: 10.3390/s150715921
    [34] Agelink MW, Malessa R, Baumann B, et al. (2001) Standardized tests of heart rate variability: normal ranges obtained from 309 healthy humans, and effects of age, gender and heart rate. Clin Auton Res 11: 99-108.
    [35] Ewing DJ, Martyn CN, Young RJ, et al. (1985) The value of cardiovascular autonomic functions tests: 10 years experience in diabetes. Diab Care 8: 491-498. doi: 10.2337/diacare.8.5.491
    [36] Pumprla J, Howorka K, Groves D, et al. (2002) Functional assessment of HRV variability: physiological basis and practical applications. Int J Cardiol 84: 1-14. doi: 10.1016/S0167-5273(02)00057-8
    [37] Ewing DJ, Campbell JW, Clarke BF (1980) The natural history of diabetic autonomic neuropathy, Q J Med 49: 95-100.
    [38] Khandoker AH, Jelinek HF, Palaniswami M (2009) Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis, Biomed Eng OnLine 8: 1-12.
    [39] Cornforth D, Jelinek HF (2007) Automated classification reveals morphological factors associated with dementia. Appl Soft Comput 8: 182-190.
    [40] Malliani A (2005) Heart rate variability: from bench to bedside. Eur J Intern Med 16: 12-20. doi: 10.1016/j.ejim.2004.06.016
    [41] Cornforth DJ, Tarvainen MP, Jelinek HF (2014) Visualization methods for assisting detection of cardiovascular neuropathy. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE; 26-30 Aug. 2014, pp. 6675-6678.
    [42] Dietrich DF, Schindler C, Schwartz J, et al. (2006) Heart rate variability in an ageing population and its association with lifestyle and cardiovascular risk factors: results of the SAPALDIA study. Europace 8: 521-529.
    [43] Ho YL, Lin C, Lin YH, et al. (2011). The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure – a pilot study of multiscale entropy. PLos One 6: 1-6.
    [44] Goldberger AL, Amaral LAN, Hausdorff JM, et al. (2002) Fractal dynamics in physiology: Alterations with disease and aging. PNAS 99: 2466-2472.
    [45] Sturmberg JP, Bennett JM, Picard M, et al. (2015) The trajectory of life. Decreasing physiological network complexity through changing fractal patterns. Front Physiol 6: 1-11.
    [46] TFESC/NASPE (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17: 354-381.
    [47] Peng CK, Havlin S, Stanley HE, et al. (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5: 82-87. doi: 10.1063/1.166141
    [48] Oida ET, Moritani KT, Yamori Y (1999) Diabetic alteration of cardiac vago-sympathetic modulation assessed with tone–entropy analysis. Acta Physiol. Scand 165: 129-135.
    [49] Lake DE, Moorman JR (2011) Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. Am J Physiol Heart Circ Physiol 300: H319-325.
    [50] Tarvainen MP, Cornforth DJ, Jelinek HF (2014) Principal component analysis of heart rate variability data in assessing cardiac autonomic neuropathy. Engineering in Medicine and Biology Society (EMBC2014), 36th Annual International Conference of the IEEE, 26-30 Aug. 2014, 6667-6670.
    [51] Witten H, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques with java implementations. 3ed, New York, Sydney: Morgan Kaufmann.
    [52] Huda S, Jelinek HF, Ray B, et al. (2010) Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a Hybrid of Wrapper-Filter based feature selection. In: Marusic S, Palaniswami M, Gubbi J, et al, editors. Intelligent sensors, sensor networks and information processing, ISSNIP 2010. Sydney: IEEE Press, 297-302.
    [53] Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10: 1895-1924.
    [54] Bouckaert, RR, Frank E, Hall M, et al., WEKA manual for version 3-7-13, http://www.cs.waikato.ac.nz/ml/weka/, viewed 15 May 2016.
    [55] Hall M, Frank E, Holmes G, et al. (2009) The WEKA data mining software: an update. SIGKDD Explor 11: 10-18.
    [56] Baumert M, Schlaich MP, Nalivaiko E, et al. (2011) Relation between QT interval variability and cardiac sympathetic activity in hypertension. Am J Physiol Heart Circ Physiol 300: H1412-1417. doi: 10.1152/ajpheart.01184.2010
    [57] Kelarev AV, Dazeley R, Stranieri A, et al. (2012) Detection of CAN by ensemble classifiers based on ripple down rules. Lect Notes Artif Int 7457: 147-159.
    [58] Kelarev AV, Stranieri A, Yearwood JL, Jelinek HF (2012) Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare. Proceedings of Network-Based Information Systems, NBIS2012, 26-28 September 2012, Melbourne, Australia, pp 441-446.
    [59] Abawajy J, Kelarev AV, Stranieri A, Jelinek HF (2012) Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing features. Workshop on New Trends of Computational Intelligence in Health Applications, CI-Health 2012. CEUR Workshop Proceed 944: 1-11.
    [60] Kelarev AV, Abawajy J, Stranieri A, Jelinek HF (2013) Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes. Int J Data Warehousing Mining 9: 1-18.
    [61] Kamsu-Foguem B, Tchuenté-Foguem G, Foguem C (2014) Conceptual graph operations for formal visual reasoning in the medical domain. IRBM—Innovation Res BioMed Eng 35: 262-270.
    [62] Kamsu-Foguem B, Tchuenté-Foguem G, Foguem C (2014) Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inform System Front16: 571-589.
    [63] Karmakar CK, Khandoker AH, Jelinek HF, et al. (2013) Risk stratification of cardiac autonomic neuropathy based on multi-lag tone–entropy. Med Biol Eng Comp 51:537-546. doi: 10.1007/s11517-012-1022-5
    [64] Chowdhury M, Abawajy J, Kelarev A, Jelinek HF (2016) A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services. IEEE Trans Cloud Comp. DOI: 10.1109/TCC.2016.2567389.
    [65] Kelarev A, Stranieri A, Yearwood J, et al. (2012) Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest. Data mining and analytics, 11th Australasian Data Mining Conference, AusDMm 2012. Conferences Res Practice Inform Techno 134: 93-102.
    [66] Williams GJ (2009) Rattle: a data mining GUI for R. R J 1: 45-55.
    [67] Williams G (2011) Data mining with Rattle and R: the art of excavating data for knowledge discovery (use R!). New York, Dordrecht, Heidelberg, London: Springer.
    [68] Chi CL, Street NK, Katz C (2010). A decision support system for cost-effective diagnosis. Artif Intell Med 50:149-161. doi: 10.1016/j.artmed.2010.08.001
    [69] Imam MH, Karmakar C, Khandoker AH, et al. (2014) Analysing cardiac autonomic neuropathy in diabetes using electrocardiogram derived systolic-diastolic interval interactions. Comp Cardiol 41: 85-88.
    [70] Abawajy J, Kelarev A, Chowdhury M, et al. (2013) Multistage approach for clustering and classification of ECG data. Comput Meth Prog Bio 112: 720-730.
    [71] Abawajy J, Kelarev A, Chowdhury M, et al. (2013) Predicting cardiac autonomic neuropathy category for diabetic data with missing values. Comput Biol Med 43: 1328-1333. doi: 10.1016/j.compbiomed.2013.07.002
    [72] Jelinek HF, Yatsko A, Stranieri A, et al. (2015) Diagnostic with incomplete nominal/discrete data. Artif Intell Res 4: 22-35.
    [73] Abawajy J, Kelarev A, Chowdhury MU, et al. (2016) Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multi-tier ensembles. IEEE J Biomed Health Informatics 20: 408-415. doi: 10.1109/JBHI.2014.2363177
    [74] Cornforth D, Tarvainen M, Jelinek HF (2014) How to calculate Rényi entropy from Heart Rate Variability, and why it matters for detecting cardiac autonomic neuropathy. Front Bioeng Biotechnol 2: 1-7.
    [75] Jelinek HF, Cornforth DJ, Kelarev AV (2017) Machine learning methods for automated detection of severe diabetic neuropathy. J Diab Compl Meds (in print).
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