Empirical Investigation of Multi-tier Ensembles for the Detection of Cardiac Autonomic Neuropathy Using Subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  Marimuthu Palaniswami,et al.  Heart rate variability and QT dispersion in a cohort of diabetes patients , 2010, 2010 Computing in Cardiology.

[3]  C. Cook,et al.  Sleep related vehicle accidents , 1995, BMJ.

[4]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[5]  D. Garvey.  A review of the social and emotional wellbeing of Indigenous Australian peoples , 2008 .

[6]  Adel Al-Jumaily,et al.  A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection , 2012, 2012 IEEE Congress on Evolutionary Computation.

[7]  S. Thompson,et al.  Health promotion resources for Aboriginal people: lessons learned from consultation and evaluation of diabetes foot care resources. , 2010, Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals.

[8]  R Walld,et al.  Utilization of health care services in patients with severe obstructive sleep apnea. , 1996, Sleep.

[9]  S W Tu,et al.  Knowledge-based temporal abstraction for diabetic monitoring. , 1994, Proceedings. Symposium on Computer Applications in Medical Care.

[10]  Asunción Gómez-Pérez,et al.  A network of ontology networks for building e-employment advanced systems , 2011, Expert Syst. Appl..

[11]  A. Miller,et al.  Obstructive sleep apnea syndrome: complications and sequelae. , 1994, The Mount Sinai journal of medicine, New York.

[12]  Jacques Wainer,et al.  Machine learning and pattern classification in identification of indigenous retinal pathology , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  M. Kryger,et al.  Epidemiology of obstructive sleep apnea: a population-based perspective , 2008, Expert review of respiratory medicine.

[14]  Andrei V. Kelarev,et al.  Clustering Algorithms for ITS Sequence Data with Alignment Metrics , 2006, Australian Conference on Artificial Intelligence.

[15]  B. Keough.  Occlusion-based treatment planning for complex dental restorations: Part 1. , 2003, The International journal of periodontics & restorative dentistry.

[16]  Sandhya Sharma,et al.  Wireless Sensor Network : Healthcare , 2014 .

[17]  K. K. Damghani,et al.  Decision Making Under Uncertain and Risky Situations , 2009 .

[18]  M. Christie,et al.  Sharing the true stories: improving communication between Aboriginal patients and healthcare workers , 2002, The Medical journal of Australia.

[19]  Andrew Stranieri,et al.  Empirical Study of Decision Trees and Ensemble Classifiers for Monitoring of Diabetes Patients in Pervasive Healthcare , 2012, 2012 15th International Conference on Network-Based Information Systems.

[20]  F. Lobbezoo,et al.  Effects of an oral appliance with different mandibular protrusion positions at a constant vertical dimension on obstructive sleep apnea , 2010, Clinical Oral Investigations.

[21]  Albert Y. Zomaya,et al.  A novel swarm based feature selection algorithm in multifunction myoelectric control , 2009, J. Intell. Fuzzy Syst..

[22]  A. Fortin.  Communication skills to improve patient satisfaction and quality of care. , 2002, Ethnicity & disease.

[23]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[24]  N. Powe,et al.  Patient race/ethnicity and quality of patient-physician communication during medical visits. , 2004, American journal of public health.

[25]  U. Mahmood,et al.  Type-2 Fuzzy Classification of Blood Pressure Parameters , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[26]  V. Moret-Bonillo,et al.  Intelligent diagnosis of sleep apnea syndrome , 2004, IEEE Engineering in Medicine and Biology Magazine.

[27]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[28]  S. Chokroverty,et al.  Overview of sleep & sleep disorders. , 2010, The Indian journal of medical research.

[29]  Kay Römer,et al.  Medium access control issues in sensor networks , 2006, CCRV.

[30]  J. Benson.  Concordance--An alternative term to 'compliance' in the Aboriginal population. , 2005, Australian family physician.

[31]  François Ingelrest,et al.  The hitchhiker's guide to successful wireless sensor network deployments , 2008, SenSys '08.

[32]  Adel Al-Jumaily,et al.  A Combined Ant Colony and Differential Evolution Feature Selection Algorithm , 2008, ANTS Conference.

[33]  Md. Rafiqul Islam,et al.  A multi-tier phishing detection and filtering approach , 2013, J. Netw. Comput. Appl..

[34]  Matthew Warren,et al.  Multi-tier Phishing Email Classification with an Impact of Classifier Rescheduling , 2009, 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks.

[35]  A. Murray,et al.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings , 2002, Medical and Biological Engineering and Computing.

[36]  P. Lloberes,et al.  Mortality in severe sleep apnoea/hypopnoea syndrome patients: impact of treatment , 2002, European Respiratory Journal.

[37]  R. van Noort.  The future of dental devices is digital. , 2012, Dental materials : official publication of the Academy of Dental Materials.

[38]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[39]  Adel Al-Jumaily,et al.  Automated detecting sleep apnea syndrome: A novel system based on genetic SVM , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[40]  A. Buj-Bello,et al.  The glossary of prosthodontic terms. , 2005, The Journal of prosthetic dentistry.

[41]  Stefan Conrad,et al.  An approach for automatic sleep stage scoring and apnea-hypopnea detection , 2010, 2010 IEEE International Conference on Data Mining.

[42]  Ronald R Grunstein,et al.  Relationship between surface facial dimensions and upper airway structures in obstructive sleep apnea. , 2010, Sleep.

[43]  D. Ewing,et al.  The natural history of diabetic autonomic neuropathy. , 1980, The Quarterly journal of medicine.

[44]  John A. Williams,et al.  Simultaneous Registration of Multiple Corresponding Point Sets , 2001, Comput. Vis. Image Underst..

[45]  Marimuthu Palaniswami,et al.  Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. , 2009, Biomedical engineering online.

[46]  Boris Villazón-Terrazas.  A Method for Reusing and Re-engineering Non-ontological Resources for Building Ontologies , 2012, Studies on the Semantic Web.

[47]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[48]  Andrew Stranieri,et al.  Detection of CAN by Ensemble Classifiers Based on Ripple Down Rules , 2012, PKAW.

[49]  Thomas H Marwick,et al.  Diabetic cardiomyopathy: evidence, mechanisms, and therapeutic implications. , 2004, Endocrine reviews.

[50]  Andrew Stranieri,et al.  Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection , 2010, 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[51]  G. Gürel.  Porcelain laminate veneers: minimal tooth preparation by design. , 2007, Dental clinics of North America.

[52]  D. Ewing,et al.  The Value of Cardiovascular Autonomic Function Tests: 10 Years Experience in Diabetes , 1985, Diabetes Care.

[53]  Gérard Bloch,et al.  Incorporating prior knowledge in support vector machines for classification: A review , 2008, Neurocomputing.

[54]  R. Connor.  Sample size for testing differences in proportions for the paired-sample design. , 1987, Biometrics.

[55]  A. Cass,et al.  “All they said was my kidneys were dead”: Indigenous Australian patients’ understanding of their chronic kidney disease , 2008, The Medical journal of Australia.

[56]  Mohammed Bennamoun,et al.  Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Johannes Fürnkranz,et al.  An Evaluation of Grading Classifiers , 2001, IDA.

[58]  Mari Carmen Suárez-Figueroa,et al.  NeOn methodology for building ontology networks: specification, scheduling and reuse , 2011, DISKI.

[59]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[60]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[61]  J. Abduo.  An innovative prostheses design for rehabilitation of severely mutilated dentition: a case report , 2011, The journal of advanced prosthodontics.

[62]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[63]  Yu Lin,et al.  Ontology driven modeling for the knowledge of genetic susceptibility to disease. , 2009, The Kobe journal of medical sciences.

[64]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[65]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[66]  Pascal Magne,et al.  Novel porcelain laminate preparation approach driven by a diagnostic mock-up. , 2004, Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.].

[67]  Andrew Stranieri,et al.  Empirical investigation of consensus clustering for large ECG data sets , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[68]  Geoffrey I. Webb,et al.  MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.

[69]  Ian H. Witten,et al.  Stacking Bagged and Dagged Models , 1997, ICML.

[70]  Thepchai Supnithi,et al.  A Semantic Web Framework to Support Knowledge Management in Chronic Disease Healthcare , 2009, MTSR.

[71]  Osman Erogul,et al.  Artificial Apnea Classification with Quantitative Sleep EEG Synchronization , 2012, Journal of Medical Systems.

[72]  Jyrki Wallenius,et al.  Multiple Criteria Decision Making: From Early History to the 21st Century , 2011 .

[73]  Peretz Lavie,et al.  Obstructive sleep apnoea syndrome as a risk factor for hypertension: population study , 2000, BMJ : British Medical Journal.

[74]  Adel Al-Jumaily,et al.  Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control , 2009, Pattern Recognit. Lett..

[75]  M.O. Al-Jaafreh,et al.  New Model to estimate Mean Blood Pressure by Heart Rate with Stroke Volume changing influence , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[76]  Adel Al-Jumaily,et al.  Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome , 2011 .

[77]  J. Stradling,et al.  Relation between systemic hypertension and sleep hypoxaemia or snoring: analysis in 748 men drawn from general practice. , 1990, BMJ.

[78]  I. Song,et al.  Working Set Selection Using Second Order Information for Training Svm, " Complexity-reduced Scheme for Feature Extraction with Linear Discriminant Analysis , 2022 .

[79]  G. Davis,et al.  Quantification of residual dentine thickness following crown preparation. , 2012, Journal of dentistry.

[80]  C. George.  Reduction in motor vehicle collisions following treatment of sleep apnoea with nasal CPAP , 2001, Thorax.

[81]  W C Dement,et al.  Diagnosis and treatment of sleep apnea within the community. The Walla Walla Project. , 1997, Archives of internal medicine.

[82]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[83]  Herbert F. Jelinek,et al.  Automated classification reveals morphological factors associated with dementia , 2008, Appl. Soft Comput..

[84]  Thepchai Supnithi,et al.  Ontology Development for Type II Diabetes Mellitus Clinical Support System , 2009 .

[85]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[86]  Ron Kohavi,et al.  Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.

[87]  T. Prvan,et al.  Prediction of obstructive sleep apnea with craniofacial photographic analysis. , 2009, Sleep.

[88]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[89]  Mark C. Jones.  PRINCIPLES AND PRACTICE OF SLEEP MEDICINE , 1990 .

[90]  Daniel J Buysse,et al.  Sleep–Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research , 2000 .

[91]  Carlos A. Pomalaza-Raez,et al.  Application-Driven Analytic Toolbox for WSNs , 2009, ADHOC-NOW.

[92]  Byeong Ho Kang,et al.  Experimental investigation of classification algorithms for ITS dataset , 2008 .

[93]  R. Conley,et al.  Evidence for dental and dental specialty treatment of obstructive sleep apnoea. Part 1: the adult OSA patient and Part 2: the paediatric and adolescent patient. , 2011, Journal of oral rehabilitation.

[94]  T. Morgenthaler,et al.  Complex sleep apnea syndrome: is it a unique clinical syndrome? , 2006, Sleep.

[95]  Marjorie Kagawa-Singer,et al.  A Strategy to Reduce Cross‐cultural Miscommunication and Increase the Likelihood of Improving Health Outcomes , 2003, Academic medicine : journal of the Association of American Medical Colleges.

[96]  Elena Hernández-Pereira,et al.  An intelligent system for the detection and interpretation of sleep apneas , 2003, Expert Syst. Appl..

[97]  M. Kanter,et al.  Proactive office encounter: a systematic approach to preventive and chronic care at every patient encounter. , 2010, The Permanente journal.

[98]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[99]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[100]  Andrei V. Kelarev,et al.  A New Model for Classifying DNA Code Inspired by Neural Networks and FSA , 2006, PKAW.

[101]  Xiangjian He,et al.  Performance Uncertainty Impact on WSNs Design Evaluation , 2012, 2012 International Conference on Control Engineering and Communication Technology.

[102]  B. Stegenga,et al.  Effects of a mandibular advancement device on the upper airway morphology: a cephalometric analysis. , 2009, Journal of oral rehabilitation.

[103]  H Bearpark,et al.  Snoring and sleep apnea. A population study in Australian men. , 1995, American journal of respiratory and critical care medicine.

[104]  Ronald R Grunstein,et al.  Craniofacial phenotyping in obstructive sleep apnea--a novel quantitative photographic approach. , 2009, Sleep.