Assistant Professor
Assistant Professor
Preventive Medicine

MEMPHIS TN 381032893
Tel: (901) 287-5841


  • PostDoc, University of Calgary, Statistics
  • PostDoc, University of Tennessee, Knoxville, Statistics
  • Ph.D., Istanbul University, Quantitative Methods
  • M.S., Mimar Sinan University, Statistics
  • B.S., Istanbul University, Mathematics

Research Keywords

Predictive Modeling, Data Mining, Neural Networks, Statistics, Classification, Prediction, Clustering

Research Interest/Specialty

Predictive Modeling, Data Mining, Neural Networks, Statistics, Classification, Prediction, Clustering, Clinical Informatics, Clinical Decision Support Systems


  1. O. Akbilgic, J.A. Howe. Symbolic Pattern Recognition for Sequential Data. Sequential Analysis (In Review).
  2. M.R. Langham Jr, O. Akbilgic, E. Huang, T. Jones, A. Walter, R.L. Davis. A Simple Decision Support Tool for Surgery in Children Utilizing NSQIP-Pediatric Data. Presented at ACS NSQIP 2016 Conference, July 16-17, 2016, San Diego, CA..
  3. R Mahajan, T Viangteeravat, O Akbilgic. Boosting the performance of symbolic pattern recognition by feature selection: A case study on detecting cardiac abnormalities. Artificial Intelligence in Medicine Conference, December 12-15, 2016, Dana point, CA.
  4. T. Viangteeravat, V.R. Nagisetty, O. Akbilgic, F. Sen, R. Mudunuri, O. Ajayi, R.L. Davis. Predicting risk of respiratory decompensation or death for hospitalized children with asthma or related conditions using machine learning techniques. Poster presentation at Pediatric Academic Societies Meeting, April 30-May 3, 2016.
  5. O. Akbilgic, M.R. Langham, Jr, A.I. Walter, T. Jones, E.Y. Huang, R.L. Davis. Accurate Risk Classification for Death within30 Days Following Surgery in Children: Classification Trees Analysis of Risk Factors Utilizing National Surgical Quality Improvement Project-Pediatric Data (In Review). Surgery, 2016.
  6. O. Akbilgic, R.L. Davis. Searching for Fingerprints of Paroxysmal Atrial Fibrialtion: A Symbolic Pattern Recognition Approach. Southern Regional Council On Statistics Summer Research Conference, 2016.
  7. M.R. Asoglu, T. Achjian, O. Akbilgic, M.A Borahay, G. Kilic. The impact of a simulation training lab on outcomes of hysterectomy. Journal of Turkish-German Gynecological Association (17), 60-64, 2016.
  8. O. Akbilgic, A.J. Howe, R.L. Davis. Categorizing Atrial Fibrillation via Symbolic Pattern Recognition. Journal of Medical Statistics and Informatics, 4 (8), 2016.
  9. P. Humez; B. Mayer; Jenifer Ing; M. Nightingale, V. Becker, A. Kingston, O. Akbilgic, S. Taylor. Occurrence and origin of methane in groundwater in Alberta (Canada): gas geochemical and isotopic approaches. Science of the Total Environment, 541, 1253--1268, 2015.
  10. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson. Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. Energy, 93 (2), 1663–1670, 2015.
  11. O. Akbilgic, M. Mahmoudkhan, G. Doluweera, J.A. Bergerson. A meta-analysis and pre- dictive analysis of CO2 avoided costs for Carbon Capture investment decisions in power plants. Applied Energy, 159, 11-18, 2015.
  12. O. Akbilgic. Classification Trees Aided Mixed Regression Model. Journal of Applied Statistics, 42 (8), 1773-1781, 2015.
  13. O. Akbilgic, D. Zhu, I.D. Gates, J.A. Bergerson. Prediction of Canadaâs oil sands GHG emissions: statistical model selection & evaluation. International Conference on Environmental Scinece & Technologies, 232-234, 2014.
  14. O. Akbilgic, H. Bozdogan, M.E. Balaban. A novel Hybrid RBF Neural Networks model as a forecaster. Statistics & Computing, 24 (3), 365-375, 2014.
  15. H. Bozdogan, O. Akbilgic. Social network analysis of scientific collaborations across different subject fields. Information Services and Use, 33 (3-4), 219-233, 2013.
  16. O. Akbilgic. Binary Classification for Hydraulic Fracturing Operations in Oil & GasWells via Tree Based Logistic RBF Networks. European Journal of Pure and Applied Mathematics, 6 (4), 377-386, 2013.
  17. O. Akbilgic. A New Supervised Classification of Credit Approval Data via The Hybrid RBF-NN Model Using The Genetic Algorithm with Information Complexity. Data Science, Learning by Latent Structures, and Knowledge Discovery,, 2013.
  18. O. Akbilgic. Tree Based Logistic RBF Neural Networks Aided Logistic regression for Binary Classification: A Case Study on Hydraulic Fracturing in Oil & Gas Well. Presented at the yBIS 2013: Joint Meeting of Young Business and Industrial Statisticians, 2013.
  19. O. Akbilgic, H. Bozdogan. Hybrid RBF Neural Network Models for Supervised Classification of Medical Data With Information Complexity And The Genetic Algorithm. National Biostatistics Conference, 2012.
  20. O. Akbilgic, H. Bozdogan. Predictive Subset Selection using Regression Trees and RBF Neural Networks Hybridized with the Genetic Algorithm. European Journal of Pure and Applied Mathematics, 4 (4), 467-485, 2011.
  21. J.A. Howe, O. Akbilgic, E. Deniz Howe. Identifying the Presence of Outliers in Regression Using LSR. 7th International Statistics Congress,, 178-179, 2011.
  22. O. Akbilgic, J.A. Howe. A Novel Normality Test Using an Identity Transformation of the Gaussian Function. European Journal of Pure and Applied Mathematics, 4 (4), 448-454, 2011.
  23. E. Deniz, O. Akbilgic, J.A. Howe. Model selection using information criteria under a new estimation method: least squares ratio. Journal of Applied Statistics, 38 (9), 2043-2050, 2011.
  24. Akinci, OF, Kurt, M, Terzi, A, Atak, I, Subasi, IE, Akbilgic, O. Natal cleft deeper in patients with pilonidal sinus: implications for choice of surgical procedure. Dis Colon Rectum, 52 (5), 1000-2, 2009.
  25. O. Akbilgic, E. Deniz Akinci. A Novel Regression Approach: Least Squares Ratio. Communications in Statistics - Theory and Methods, 38 (9), 1539-1545, 2009.
  26. O. Akbilgic, T. Keskinturk. The Comparison of Artificial Neural Networks and Regression Analysis. YONETIM, 60 (19), 74-83, 2008.
  27. F. Lorcu, O. Akbilgic. Canonical Correlation Analysis of Economical Indicators of OECD Countries. 2008 meeting of the International Conference on Business Management and Economics, 2008.
  28. O. Akbilgic, T. Keskinturk. The Comparison of Artificial Neural Networks and Regression Analysis. Yönetim Dergisi, 19 (60), 74-83, 2008.
  29. Demirkok, SS, Basaranoglu, M, Akbilgic, O. Seasonal variation of the onset of presentations in stage 1 sarcoidosis. Int J Clin Pract, 60 (11), 1443-50, 2006.
  30. Demirkok SS, Basaranoglu M, Bilir M, Akbilgic O, Karayel T. Seasonal variation of the onset of presentations in patients with sarcoidosis presented with BHL alone. 2005 September 17; Copenhagen, Denmark, 2005.