Predicting Hypoglycemia in Diabetic Patients using Machine Learning Techniques

A Master of Science thesis in Computer Engineering by Khuloud Abdel Aziz Safi Eljil entitled, "Predicting Hypoglycemia in Diabetic Patients using Machine Learning Techniques," submitted in June 2014. Thesis advisor is Dr. Ghassan Qaddah and thesis co-advisor is Dr. Michel Pasquier. Available are both soft and hard copies of the thesis.

[1]  Nada Lavrac,et al.  Selected techniques for data mining in medicine , 1999, Artif. Intell. Medicine.

[2]  Ying Zhang,et al.  Predicting occurrences of acute hypoglycemia during insulin therapy in the intensive care unit , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  D. Rogers,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus , 1994 .

[4]  G. Gibbons,et al.  Diabetic vascular disease: characteristics of vascular disease unique to the diabetic patient. , 2012, Seminars in vascular surgery.

[5]  R. Brazg,et al.  Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System , 2007, Diabetes Care.

[6]  Manaswini Pradhan,et al.  Predict the onset of diabetes disease using Artificial Neural Network (ANN) , 2011 .

[7]  P. White,et al.  Youth and parent satisfaction with clinical use of the GlucoWatch G2 Biographer in the management of pediatric type 1 diabetes. , 2005, Diabetes care.

[8]  Eyal Dassau,et al.  Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring , 2010, Diabetes Care.

[9]  M. Shichiri,et al.  Closed-loop subcutaneous insulin infusion algorithm with a short-acting insulin analog for long-term clinical application of a wearable artificial endocrine pancreas. , 1997, Frontiers of medical and biological engineering : the international journal of the Japan Society of Medical Electronics and Biological Engineering.

[10]  Novruz Allahverdi,et al.  Design of a hybrid system for the diabetes and heart diseases , 2008, Expert Syst. Appl..

[11]  J. Kiusalaas Numerical Methods in Engineering with MATLAB®: Symmetric Matrix Eigenvalue Problems , 2009 .

[12]  Vítor Santos Costa,et al.  Inductive Logic Programming , 2013, Lecture Notes in Computer Science.

[13]  Evangelos Triantaphyllou,et al.  Prediction of Diabetes by Employing a New Data Mining Approach Which Balances Fitting and Generalization , 2008, Computer and Information Science.

[14]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[15]  Andrew P. Bradley,et al.  Rule Extraction from Support Vector Machines: A Sequential Covering Approach , 2007, IEEE Transactions on Knowledge and Data Engineering.

[16]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Ngai Hang Chan Multivariate Time Series , 2011 .

[18]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[19]  E D Lehmann,et al.  A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. , 1992, Journal of biomedical engineering.

[20]  B. Peterson,et al.  Nurse Case Management To Improve Glycemic Control in Diabetic Patients in a Health Maintenance Organization , 1998, Annals of Internal Medicine.

[21]  Joachim Diederich,et al.  Eclectic Rule-Extraction from Support Vector Machines , 2005 .

[22]  Matlab Matlab (the language of technical computing): using matlab graphics ver.5 , 2014 .

[23]  Jesús Picó,et al.  Comprehensive pharmacokinetic model of insulin Glargine and other insulin formulations , 2005, IEEE Transactions on Biomedical Engineering.

[24]  W. Alexander,et al.  American diabetes association. , 2010, P & T : a peer-reviewed journal for formulary management.

[25]  Héctor Pomares,et al.  Time Series Analysis and Forecasting , 2016 .

[26]  F. Al-Maskari,et al.  Prevalence and determinants of microalbuminuria among diabetic patients in the United Arab Emirates , 2008, BMC nephrology.

[27]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[28]  Riccardo Bellazzi,et al.  Data Mining Technologies for Blood Glucose and Diabetes Management , 2009, Journal of diabetes science and technology.

[29]  Christian Binder,et al.  Modeling absorption kinetics of subcutaneous injected soluble insulin , 1989, Journal of Pharmacokinetics and Biopharmaceutics.

[30]  Rolf Johansson,et al.  Linear Modeling and Prediction in Diabetes Physiology , 2014 .

[31]  Asma A. Al Jarullah Decision tree discovery for the diagnosis of type II diabetes , 2011, 2011 International Conference on Innovations in Information Technology.

[32]  RadhaKanta Mahapatra,et al.  Business data mining - a machine learning perspective , 2001, Inf. Manag..

[33]  Torolf Ternstrom,et al.  A periodic table , 1964 .

[34]  W. Tamborlane,et al.  A tale of two compartments: interstitial versus blood glucose monitoring. , 2009, Diabetes technology & therapeutics.

[35]  N. Dubin Mathematical Model , 2022 .

[36]  Werner Scrulze Insulin and Glucagon , 2008 .

[37]  Michael J. Shaw,et al.  Knowledge management and data mining for marketing , 2001, Decis. Support Syst..

[38]  Andrew P. Bradley,et al.  Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus , 2010, IEEE Transactions on Information Technology in Biomedicine.

[39]  Evangelos Triantaphyllou,et al.  The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[40]  Fevzullah Temurtas,et al.  A comparative study on diabetes disease diagnosis using neural networks , 2009, Expert Syst. Appl..

[41]  E. Travis Littledike,et al.  Insulin , 1923, The Indian medical gazette.

[42]  J. Mastrototaro,et al.  The accuracy and efficacy of real-time continuous glucose monitoring sensor in patients with type 1 diabetes. , 2008, Diabetes technology & therapeutics.

[43]  Jae-Eung Lee Linear multi-stage identification of ARMAX processes with stochastic structural dynamics applications. , 1991 .

[44]  Karin Wårdell,et al.  Trend estimation of blood glucose level fluctuations based on data mining , 2003 .

[45]  Dimitrios I. Fotiadis,et al.  Glucose Prediction in Type 1 and Type 2 Diabetic Patients Using Data Driven Techniques , 2011 .

[46]  Claudio Cobelli,et al.  A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.

[47]  Joachim Diederich,et al.  Rule Extraction from Support Vector Machines , 2008, Studies in Computational Intelligence.

[48]  C. Cobelli,et al.  Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. , 2010, Diabetes technology & therapeutics.

[49]  Hao Yu,et al.  Levenberg—Marquardt Training , 2011 .

[50]  Z. Trajanoski,et al.  Numerical approximation of mathematical model for absorption of subcutaneously injected insulin , 2006, Medical and Biological Engineering and Computing.

[51]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[52]  Patricia Melin,et al.  Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition , 2009, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition.

[53]  Parvez Hossain,et al.  Obesity and diabetes in the developing world--a growing challenge. , 2007, The New England journal of medicine.

[54]  Ruben D. Canlas Data Mining in Healthcare : Current Applications and Issues By , 2010 .

[55]  Masashi Kobayashi,et al.  Prediction of blood glucose level of type 1 diabetics using response surface methodology and data mining , 2006, Medical and Biological Engineering and Computing.

[56]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[57]  F. Doyle,et al.  Prevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension , 2010, Diabetes Care.

[58]  H. King,et al.  High Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance in the Sultanate of Oman: Results of the 1991 National Survey , 1995, Diabetic medicine : a journal of the British Diabetic Association.