Intelligent type 2 diabetes risk prediction from administrative claim data

References

• AIHW. Health expenditure Australia 2017–18. Vol. 2019. Canberra (ACT): Australian Institute of Health and Welfare; 2019.
   Google Scholar
• AIHW. Disease expenditure in Australia. Vol. 2019. Canberra (ACT): Australian Institute of Health and Welfare; 2019.
   Google Scholar
• Moini J. 2019 Epidemiology of Diabetes. Cambridge, United States: Elsevier; 2019 Mar 19 211.
   Google Scholar
• Hossain ME, Khan A, Moni MA, and Uddin S. Use of electronic health data for disease prediction: a comprehensive literature review. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(2): 745–758 .
   PubMed Web of Science ®Google Scholar
• Khan A, Uddin S, Srinivasan U. 2018Comorbidity network for chronic disease: a novel approach to understand type 2 diabetes progression. International Journal of Medical Informatics. 115:1–9. doi:10.1016/j.ijmedinf.2018.04.001.
   PubMed Web of Science ®Google Scholar
• Nahar J, Imam T, Tickle KS, Chen YPP. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications. 2013;40(4):1086–93. doi:10.1016/j.eswa.2012.08.028.
   Web of Science ®Google Scholar
• Lu H, Uddin S, Hajati F, Moni MA, and Khushi M. A patient network-based machine learning model for disease prediction: the case of type 2 diabetes mellitus. Applied Intelligence. 2021;1–12. doi:10.1007/s10489-021-02533-w.
   PubMedGoogle Scholar
• Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: an overview of heart disease prediction. International Journal of Computer Applications. 2011;17(8):43–48. doi:10.5120/2237-2860.
   Google Scholar
• Aneja S, and Lal S, editors. Effective asthma disease prediction using naive Bayes—neural network fusion technique. International Conference on Parallel, Distributed and Grid Computing (PDGC). Solan, India: IEEE; 2014.
   Google Scholar
• Ani R, Sasi G, Sankar UR, and Deepa O, editors. Decision support system for diagnosis and prediction of chronic renal failure using random subspace classification. Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference. Jaipur, India: IEEE; 2016.
   Google Scholar
• Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. SpringerPlus. 2016;5(1):701. doi:10.1186/s40064-016-2339-6.
   PubMedGoogle Scholar
• Mani S, Chen Y, Elasy T, Clayton W, and Denny J, editors. Type 2 diabetes risk forecasting from EMR data using machine learning. AMIA annual symposium proceedings. Chicago, United States: American Medical Informatics Association; 2012.
   Google Scholar
• Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, Kim N. Deep learning in medical imaging: general overview. Korean Journal of Radiology. 2017;18(4):570–84. doi:10.3348/kjr.2017.18.4.570.
   PubMed Web of Science ®Google Scholar
• Khan A, Uddin S, Srinivasan U. Chronic disease prediction using administrative data and graph theory: the case of type 2 diabetes. Expert Systems with Applications. 2019;136:230–41. doi:10.1016/j.eswa.2019.05.048.
   Web of Science ®Google Scholar
• Akter T, Satu MS, Khan MI, Ali MH, Uddin S, Lió P, Quinn JMW, Moni MA. Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access. 2019;7(1):166509–27. doi:10.1109/ACCESS.2019.2952609.
   Google Scholar
• Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences. 2000;97(1):262–67. doi:10.1073/pnas.97.1.262.
   PubMed Web of Science ®Google Scholar
• Hosseinzadeh A, Izadi M, Verma A, Precup D, Buckeridge D, editors. Assessing the predictability of hospital readmission using machine learning2013. 2013.
   Google Scholar
• Kirlidog M, Asuk C. A fraud detection approach with data mining in health insurance. Procedia - Social and Behavioral Sciences. 2012;62:989–94. doi:10.1016/j.sbspro.2012.09.168.
   Google Scholar
• Ortega PA, Figueroa CJ, Ruz GA. A medical claim fraud/abuse detection system based on data mining: a case study in chile. DMIN. 2006;6:26–29.
   Google Scholar
• Sheshasaayee A, Thomas SS, editors. A purview of the impact of supervised learning methodologies on health insurance fraud detection2018. Singapore: Springer; 2018.
   Google Scholar
• Choi SB, Lee W, Yoon J-H, Won J-U, Kim DW. Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea. Journal of Affective Disorders. 2018;231:8–14. doi:10.1016/j.jad.2018.01.019.
   PubMed Web of Science ®Google Scholar
• Kose I, Gokturk M, Kilic K. An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing. 2015;36:283–99. doi:10.1016/j.asoc.2015.07.018.
   Web of Science ®Google Scholar
• Kähm K, Stark R, Laxy M, Schneider U, and Leidl R. Assessment of excess medical costs for persons with type 2 diabetes according to age groups: an analysis of German health insurance claims data. Diabetic Medicine. 2020;37(10): 1752–1758.
   PubMed Web of Science ®Google Scholar
• Flory J, Gerhard T, Stempniewicz N, Keating S, Rowan CG. Comparative adherence to diabetes drugs: an analysis of electronic health records and claims data. Diabetes, Obesity and Metabolism. 2017;19(8):1184–87. doi:10.1111/dom.12931.
   PubMed Web of Science ®Google Scholar
• Deo R, and Panigrahi S (), editors. Performance assessment of machine learning based models for diabetes prediction. 2019 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT); November 2019; Bethesda, USA: 2019.
   Google Scholar
• Lee CH, Yoon H-J. Medical big data: promise and challenges. Kidney Research and Clinical Practice. 2017;36(1):3. doi:10.23876/j.krcp.2017.36.1.3.
   PubMed Web of Science ®Google Scholar
• Roberts RF, Innes KC, Walker SM. Introducing ICD-10-AM in Australian hospitals. The Medical Journal of Australia. 1998;169:S32–5.
   PubMed Web of Science ®Google Scholar
• Albrecht JS, Hanna M, Kim D, Perfetto EM. Predicting diagnosis of Alzheimer’s disease and related dementias using administrative claims. Journal of Managed Care & Specialty Pharmacy. 2018;24(11):1138–45. doi:10.18553/jmcp.2018.24.11.1138.
   PubMed Web of Science ®Google Scholar
• Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care. 1998;36(1):8–27. doi:10.1097/00005650-199801000-00004.
   PubMed Web of Science ®Google Scholar
• Austin SR, Wong Y-N, Uzzo RG, Beck JR, Egleston BL. Why summary comorbidity measures such as the Charlson comorbidity index and elixhauser score work. Medical Care. 2015;53(9):e65–e72. doi:10.1097/MLR.0b013e318297429c.
   PubMed Web of Science ®Google Scholar
• Chang H-J, Chen P-C, Yang -C-C, Su Y-C, Lee -C-C. Comparison of elixhauser and Charlson methods for predicting oral cancer survival. Medicine. 2016;95(7):e2861. doi:10.1097/MD.0000000000002861.
   PubMed Web of Science ®Google Scholar
• Menendez ME, Neuhaus V, van Dijk CN, Ring D. The elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clinical Orthopaedics and Related Research. 2014;472(9):2878–86. doi:10.1007/s11999-014-3686-7.
   PubMed Web of Science ®Google Scholar
• Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi J-C, Saunders LD, Beck CA, Feasby TE, Ghali WA, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care. 2005;43(11):1130–39. doi:10.1097/01.mlr.0000182534.19832.83.
   PubMed Web of Science ®Google Scholar
• Magnin B, Mesrob L, Kinkingnéhun S, Pélégrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehéricy S, Benali H. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology. 2009;51(2):73–83. doi:10.1007/s00234-008-0463-x.
   PubMed Web of Science ®Google Scholar
• Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neuroscience & Biobehavioral Reviews. 2012;36(4):1140–52. doi:10.1016/j.neubiorev.2012.01.004.
   PubMed Web of Science ®Google Scholar
• Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making. 2019;19(1):1–16. doi:10.1186/s12911-019-1004-8.
   PubMed Web of Science ®Google Scholar
• Pan A, Wang Y, Talaei M, Hu FB, Wu T. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. The Lancet Diabetes & Endocrinology. 2015;3(12):958–67. doi:10.1016/S2213-8587(15)00316-2.
   PubMed Web of Science ®Google Scholar
• Bergmeir C, Hyndman RJ, Koo B. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis. 2018;120:70–83. doi:10.1016/j.csda.2017.11.003.
   Web of Science ®Google Scholar
• Krstajic D, Buturovic LJ, Leahy DE, Thomas S. Cross-validation pitfalls when selecting and assessing regression and classification models. Journal of Cheminformatics. 2014;6(1):10. doi:10.1186/1758-2946-6-10.
   PubMed Web of Science ®Google Scholar
• Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 2017;27(5):1413–32. doi:10.1007/s11222-016-9696-4.
   Web of Science ®Google Scholar
• dos Santos P, Araujo I, Sousa R, and Silva A, editors. Quantum enhanced k-fold cross-validation. 2018 7th Brazilian Conference on Intelligent Systems (BRACIS); Sao Paulo, Brazil: IEEE; 2018.
   Google Scholar
• Probst P, Bischl B, Boulesteix A-L. Tunability: importance of hyperparameters of machine learning algorithms. arXiv Preprint. 2018;arXiv:180209596.
   Google Scholar
• Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A Hyperband: a novel bandit-based approach to hyperparameter optimization. arXiv preprint. arXiv:160306560. 2016.
   Google Scholar
• Bergstra JaB Y. Random search for hyper-parameter optimization. Journal of Machine Learning Research. 2012;13:281–305.
   Web of Science ®Google Scholar
• Mania H, Guy A, Recht B. Simple random search provides a competitive approach to reinforcement learning. arXiv Preprint. 2018;arXiv:180307055.
   Google Scholar
• Ting KM Confusion matrix. encyclopedia of machine learning and data mining. 260. 2017.
   Google Scholar
• Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006;27(8):861–74. doi:10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Witten IH, Frank E Data mining: practical machine learning tools and techniques: Morgan Kaufmann. 2005.
   Google Scholar
• Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2016;374(2065):20150202. doi:10.1098/rsta.2015.0202.
   PubMed Web of Science ®Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel, M, Prettenhofer, P, Weiss, R, and Dubourg, V. Scikit-learn: machine learning in Python. Journal of Machine Learning Research. 2011 Oct;12:2825–30.
   Web of Science ®Google Scholar
• Krawczyk B. Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence. 2016;5(4):221–32. doi:10.1007/s13748-016-0094-0.
   Web of Science ®Google Scholar
• Chawla NV. Data mining for imbalanced datasets: an overview. In: Maimon, O, and Rokach, L, eds. Data mining and knowledge discovery handbook. Springer; 2009:875–86.
   Google Scholar
• Chawla NV. Data mining for imbalanced datasets: an overview. In: Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. Boston (MA): Springer US; 2005. p. 853–67.
   Google Scholar
• Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53. doi:10.2337/diacare.27.5.1047.
   PubMed Web of Science ®Google Scholar
• Control CfD. Prevention. national diabetes statistics report: estimates of diabetes and its burden in the United States, 2014. Vol. 2014. Atlanta (GA): US Department of Health and Human Services; 2014.
   Google Scholar
• Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Nilsson PM, Gudbjörnsdottir S. Risk prediction of cardiovascular disease in type 2 diabetes: a risk equation from the Swedish national diabetes register. Diabetes Care. 2008;31(10):2038–43. doi:10.2337/dc08-0662.
   PubMed Web of Science ®Google Scholar
• Hossain ME, Khan A, Uddin S, editors. Understanding the progression of congestive heart failure of type 2 diabetes patient using disease network and hospital claim data. International Conference on Complex Networks and Their Applications. Springer; 2019.
   Google Scholar
• Zhou B, Lu Y, Hajifathalian K, Bentham J, Di Cesare M, Danaei G, Bixby, H, Cowan, MJ, Ali, MK, and Taddei, C. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4· 4 million participants. The Lancet. 2016;387(10027):1513–30.
   PubMed Web of Science ®Google Scholar
• Xuan P, Pan S, Zhang T, Liu Y, Sun H. Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations. Cells. 2019;8:9.
   Web of Science ®Google Scholar
• WHO Mortality Database [online database]. Geneva: world health organization. 2020. [Accessed 10 January 2020]. https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=2E1E99925345BDA39580E4FA34043B60?sequence=1
   Google Scholar
• Patil BM, Joshi RC, Toshniwal D. Hybrid prediction model for Type-2 diabetic patients. Expert Systems with Applications. 2010;37(12):8102–08. doi:10.1016/j.eswa.2010.05.078.
   Web of Science ®Google Scholar
• Kaur G, Improved CA. J48 Classification Algorithm for the Prediction of Diabetes. International Journal of Computer Applications. 2014;98(22):13–17. doi:10.5120/17314-7433.
   Google Scholar
• Kandhasamy JP, Balamurali S. Performance analysis of classifier models to predict diabetes mellitus. Procedia Computer Science. 2015;47:45–51. doi:10.1016/j.procs.2015.03.182.
   Google Scholar
• Kahn R, Alperin P, Eddy D, Borch-Johnsen K, Buse J, Feigelman J, Gregg E, Holman RR, Kirkman MS, Stern M, et al. Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis. The Lancet. 2010;375(9723):1365–74. doi:10.1016/S0140-6736(09)62162-0.
   PubMed Web of Science ®Google Scholar
• Halpern A, Mancini MC, MEC M, Fisberg M, Radominski R, Bertolami MC, Bertolami A, de Melo ME, Zanella MT, Queiroz MS, et al. Metabolic syndrome, dyslipidemia, hypertension and type 2 diabetes in youth: from diagnosis to treatment. Diabetology & Metabolic Syndrome. 2010;2(1):55. doi:10.1186/1758-5996-2-55.
   PubMedGoogle Scholar
• Rana JS, Mittleman MA, Sheikh J, Hu FB, Manson JE, Colditz GA, Speizer FE, Barr RG, Camargo CA. Chronic obstructive pulmonary disease, asthma, and risk of type 2 diabetes in women. Diabetes Care. 2004;27(10):2478–84. doi:10.2337/diacare.27.10.2478.
   PubMed Web of Science ®Google Scholar
• Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. Jama. 2007;298(22):2654–64. doi:10.1001/jama.298.22.2654.
   PubMed Web of Science ®Google Scholar
• Diabetes Australia. Available from: https://www.diabetesaustralia.com.au/risk-calculator
   Google Scholar
• Bommer C, Sagalova V, Heesemann E, Manne-Goehler J, Atun R, Bärnighausen T, Davies J, Vollmer S. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 2018;41(5):963–70. doi:10.2337/dc17-1962.
   PubMed Web of Science ®Google Scholar
• Dall TM, Yang W, Gillespie K, Mocarski M, Byrne E, Cintina I, Beronja K, Semilla AP, Iacobucci W, Hogan PF, et al. The economic burden of elevated blood glucose levels in 2017: diagnosed and undiagnosed diabetes, gestational diabetes mellitus, and prediabetes. Diabetes Care. 2019;42(9):1661–68. doi:10.2337/dc18-1226.
   PubMed Web of Science ®Google Scholar