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Using Data Mining Techniques in Monitoring Diabetes Care. The Simpler the Better?

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Abstract

We aim at evaluating how data-mining statistical techniques can be applied on medical records and administrative data of diabetes and how they differ in terms of capabilities of predicting outcomes (e.g. death). Data on 3,892 outpatient patients with a diagnosis of type 2 diabetes from the San Giovanni Battista Hospital in Torino. Six statistical classifiers were applied: Logistic regression (LR), Generalized Additive Model (GAM), Projection pursuit Regression (PPR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Artificial Neural Networks (ANN). All models selected the same subset of covariates. ANN is the model performing worse, whereas simpler models, like LR, GAM and LDA seem to perform better. GAM is associated with a very small misclassification rate. The agreement in predicting individual outcomes among models is 0.23 (SE 0.06, Kappa). Monitoring on the basis of patients’ characteristics is highly dependent from the statistical properties of the chosen statistical model.

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References

  1. Podgorelec, V., Kokol, P., Stiglic, M. M., Hericko, M., and Rozman, I., Knowledge discovery with classification rules in a cardiovascular dataset. Comput. Methods Programs Biomed. 80(Suppl 1):S39–S49, 2005.

    Article  Google Scholar 

  2. Zhang, Q. P., Sun, D. Y., Lu, M., Qin, P., and Shang, T., The application of biomed-informatics in cardiovascular research—Data and knowledge. Sheng Li Ke Xue Jin Zhan. 36(2):119–124, 2005.

    Google Scholar 

  3. Bo, S., Ciccone, G., Grassi, G., et al., Patients with type 2 diabetes had higher rates of hospitalization than the general population. J. Clin. Epidemiol. 57(11):1196–1201, 2004.

    Article  Google Scholar 

  4. R Development Core Team. R: A language and environment for statistical computing 2005.

  5. Fisher, R. A., The use of multiple measurements in taxonomic problems. Annals of Eugenics. 8:376–386, 1936.

    Google Scholar 

  6. Tatsuoka, M. M., Discriminant analysis. Institute for Personality and Ability Testing, Champaign, 1970.

    Google Scholar 

  7. Nelder, J. A., and Wedderburn, R. W. M., Generalized linear models. J. R. Stat. Soc., Ser. A. 135:370–384, 1972.

    Article  Google Scholar 

  8. Hastie, T. J., and Tibshirani, R. J., Generalized additive models. Chapman and Hall, New York, 1990.

    MATH  Google Scholar 

  9. Friedman, J. H., and Stuetzle, W., Projection pursuit regression. J. Am. Stat. Assoc. 76:817–823, 1981.

    Article  MathSciNet  Google Scholar 

  10. Ripley, B. D., Pattern recognition and neural networks. Cambridge University Press, Cambridge, 1996.

    MATH  Google Scholar 

  11. Efron, B., Estimating the error rate of a prediction rule: Some improvements on crossvalidation. J. Am. Stat. Assoc. 78:316–331, 1983.

    Article  MathSciNet  MATH  Google Scholar 

  12. Siegel, S. and Castellan, J. N. Nonparametric statistics for the behavioral sciences. 2nd ed. McGraw-Hill, 1988.

  13. Bartfay, E., Mackillop, W. J., and Prater, J. L., Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients. Eur. J. Cancer Care. 15(2):115–124, 2006.

    Article  Google Scholar 

  14. Braitman, L. E., and Davidoff, F., Predicting clinical states in individual patients. Ann. Intern. Med. 125(5):406–412, 1996.

    Google Scholar 

  15. Reilly, B. M., and Evans, A. T., Translating clinical research into clinical practice: Impact of using prediction rules to make decisions. Ann. Intern. Med. 144(3):201–209, 2006.

    Google Scholar 

  16. Scott, L. J., Warram, J. H., Hanna, L. S., Laffel, L. M., Ryan, L., and Krolewski, A. S., A nonlinear effect of hyperglycemia and current cigarette smoking are major determinants of the onset of microalbuminuria in type 1 diabetes. Diabetes. 50(12):2482–2489, 2001.

    Article  Google Scholar 

  17. Andersen, A. H., Gash, D. M., and Avison, M. J., Principal component analysis of the dynamic response measured by fMRI: A generalized linear systems framework. Magn. Reson. Imaging. 17(6):795–815, 1999.

    Article  Google Scholar 

  18. Du, Y., and Liang, Y., Data mining for seeking accurate quantitative relationship between molecular structure and GC retention indices of alkanes by projection pursuit. Comput. Biol. Chem. 27(3):339–353, 2003.

    Article  Google Scholar 

  19. Du, Y., Liang, Y., and Yun, D., Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit. J. Chem. Inf. Comput. Sci. 42(6):1283–1292, 2002.

    Google Scholar 

  20. Gribonval, R., From projection pursuit and CART to adaptive discriminant analysis? IEEE Trans. Neural Netw. 16(3):522–532, 2005.

    Article  Google Scholar 

  21. Ren, S., and Kim, H., Comparative assessment of multiresponse regression methods for predicting the mechanisms of toxic action of phenols. J. Chem. Inf. Comput. Sci. 43(6):2106–2110, 2003.

    Google Scholar 

  22. Vlassis, N., Motomura, Y., and Krose, B., Supervised dimension reduction of intrinsically low-dimensional data. Neural Comput. 14(1):191–215, 2002.

    Article  MATH  Google Scholar 

  23. Ennis, M., Hinton, G., Naylor, D., Revow, M., and Tibshirani, R., A comparison of statistical learning methods on the GUSTO database. Stat. Med. 17:2501–2508, 1998.

    Article  Google Scholar 

  24. Almeida, J. S., Predictive non-linear modeling of complex data by artificial neural networks. Curr. Opin. Biotechnol. 13(1):72–76, 2002.

    Article  Google Scholar 

  25. Tafeit, E., and Reibnegger, G., Artificial neural networks in laboratory medicine and medical outcome prediction. Clin. Chem. Lab. Med. 37(9):845–853, 1999.

    Article  Google Scholar 

  26. Tu, J. V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49:1225–1231, 1996.

    Article  Google Scholar 

  27. Schwarzer, G., Vach, W., and Schumacher, M., On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat. Med. 19(4):541–561, 2000.

    Article  Google Scholar 

  28. Ripley, B. D. Statistical aspects of neural networks. In: Barndorff-Nielsen, O. E., JJLe, ed. Networks and chaos—statistical and probabilistic aspects. London: Chapman and Hall, 1993.

  29. Vach, W., Rossner, R., and Schumacher, M., Neural networks and logistic regression: Part II. Comput. Stat. Data Anal. 21:683–701, 1996.

    Article  MATH  Google Scholar 

  30. Dybowski, R., Weller, P., Chang, R., and Gant, V., Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet. 347(9009):1146–1150, 1996.

    Article  Google Scholar 

  31. Justice, A. C., Covinsky, K. E., and Berlin, J. A., Assessing the generalizability of prognostic information. Ann. Intern. Med. 130(6):515–524, 1999.

    Google Scholar 

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Authors and Affiliations

  1. Laboratories of Epidemiological Methods and Biostatistics, Department of Environmental Medicine and Public Health, University of Padova, Via Loredan 18, 35121, Padova, Italy

    Dario Gregori

  2. Unit of Cancer Epidemiology, University of Torino, and CPO Piemonte, Turin, Italy

    Michele Petrinco, Rosalba Rosato, Eva Pagano & Franco Merletti

  3. Department of Internal Medicine, University of Torino, Turin, Italy

    Simona Bo

  4. Department of Public Health and Microbiology, University of Torino, Turin, Italy

    Paola Berchialla

Authors
  1. Dario Gregori
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  2. Michele Petrinco
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  3. Simona Bo
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  4. Rosalba Rosato
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  5. Eva Pagano
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  6. Paola Berchialla
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  7. Franco Merletti
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Correspondence to Dario Gregori.

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Gregori, D., Petrinco, M., Bo, S. et al. Using Data Mining Techniques in Monitoring Diabetes Care. The Simpler the Better?. J Med Syst 35, 277–281 (2011). https://doi.org/10.1007/s10916-009-9363-9

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  • DOI: https://doi.org/10.1007/s10916-009-9363-9

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