A Study on Significant Predictors for Prediction of Undiagnosed T2DM Using Binary Logistic Regression Model

  • Authors

    • S. S. N. Zainal
    • M. J. Masnan
    • A. Ahmed
    • N. A. M. Amin
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22309
  • Binary Logistic Regression model, Significant predictors, undiagnosed T2DM

  • Type 2 Diabetes Mellitus (T2DM) is a chronic disease that can cause premature deaths worldwide. Malaysia is one of the many countries that facing this serious epidemic. The World Health Organization (WHO) has also estimated that Malaysia would have 2.8 million people having T2DM disease in 2030. This study aims to identify significant predictors for prediction of undiagnosed T2DM patients in one of the highest prevalence states of T2DM. Binary logistic regression model proposed to predict the presence of T2DM among undiagnosed respondents. The selection of significant predictors using univariate, multivariate and backward stepwise selection was implemented in this study. The study concludes that four predictors were found significant for prediction of undiagnosed T2DM patients.

  • References

    1. [1] Bagheri N et al (2014), Undiagnosed diabetes from cross-sectional GP practice data: an approach to identify communities with high likelihood of undiagnosed diabetes. BMJ Open, vol. 4, no. 7, pp. 1–10.

      [2] Zainal SSN, Masnan MJ, Amin NAM & Mohamed N (2017), Spatial analysis for prevalence of type 2 diabetes mellitus - A state investigation, AIP Conference Proceedings, vol. 1905, pp. 1–6.

      [3] World Health Organization (WHO), Global Report on Diabetes, International Standard Book Number, ISBN, France:WHO (2016).

      [4] The Third National Health and Morbidity Survey 2006 (NHMS III): Diabetes Mellitus, Malaysia: Institute for Public Health (2008).

      [5] National Health and Morbidty Survey 2011 (NHMS 2011): Non-Communicable Diseases, Malaysia: Institute for Public Health (2011).

      [6] National Health and Morbidity Survey 2015 (NHMS 2015): Communicable Diseases, Risk Factors & Other Health Problems, Malaysia: Institute for Public Health (2015).

      [7] “3.6 juta rakyat Malaysia hidap diabetes,†Berita Harian Online, para. 2, November 15, 2017. [Online]. Available: https://www.bharian.com.my. [Accessed Dec. 20, 2017].

      [8] Meng XH, Huang YX, Rao DP, Zhang Q & Liu Q (2013), Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J. Med. Sci., vol. 29, no. 2, pp. 93–99.

      [9] Bonora E et al. (2004), Population-Based Incidence Rates and Risk Factors for Type 2 Diabetes in White Individuals: The Bruneck Study,†vol. 53, no. 7, pp. 1782-1789.

      [10] Ganz ML, Wintfeld N, Li Q, Alas V, Langer J & Hammer M (2014), The association of body mass index with the risk of type 2 diabetes: a case–control study nested in an electronic health records system in the United States. Diabetol. Metab. Syndr., vol. 6, no. 1, p. 50.

      [11] Tabachnick BG & Fidell LS, Using Multivariate Statistics, 5th ed. United States of America: Person Education Inc (2007).

      [12] Dugee O et al. (2015), Adapting existing diabetes risk scores for an Asian population: a risk score for detecting undiagnosed diabetes in the Mongolian population., BMC Public Health, vol. 15, no. 1, pp. 1–9.

      [13] Tabaei BP & Herman WH (2002), A Multivariate Logistic Regression Equation to Screen for Diabetes, Diabetes Care, vol. 25, no. 11, pp. 1–5.

      [14] Hosmer DW & Lemeshow S (2000), Applied Logistic Regression, 2nd ed., no. 1, United States of America: John Wiley & Sons Inc.

      [15] Park HA (2013), An introduction to Logistic Regression: from basic concepts to interpretation with particular attention to nursing domain. J. Korean Acad. Nurs., vol. 43, no. 2, pp. 154.

      [16] Al-Ghamdi AS (1997), Using Logistic Regression for estimating the influence of some accident factors on severity,†Accid. Anal. Prev., vol. 34, pp. 729–741.

      [17] Pramono LA et al. (2010), Prevalence and predictors of undiagnosed diabetes mellitus in Indonesia. Acta Med. Indones., vol. 42, no. 4, pp. 216–223.

      [18] Tetrault JM, Sauler M, Wells CK & Concato J (2008), Reporting of multivariable methods in the medical literature. J. Investig. Med., vol. 56, no. 7, pp. 954–957.

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  • How to Cite

    Zainal, S. S. N., Masnan, M. J., Ahmed, A., & Amin, N. A. M. (2018). A Study on Significant Predictors for Prediction of Undiagnosed T2DM Using Binary Logistic Regression Model. International Journal of Engineering & Technology, 7(4.30), 355-358. https://doi.org/10.14419/ijet.v7i4.30.22309