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A Systematic Review on Extracting Predictors for Forecasting Complications of Diabetes Mellitus

Published: 26 October 2021 Publication History

Abstract

The high global prevalence, irreversible health burden, and expenditures of diabetes mellitus lead to a proliferative research area of prognosis and diagnosis of diabetes and its complications through machine learning techniques. Although the risk scoring and prediction models have been proposed for decades, challenges still persist. Feature selection is one challenging problem that is still remains when building accurate models. Since each risk factor's contribution in predicting complications of diabetes vary with the recognition of novel risk factors, it is a requirement to make an up-to-date standard feature subset that can predict the risk of complications of diabetes mellitus. This research in progress paper proposes a systematic review study that aims to extract frequent feature subsets for predicting complications of diabetes. Diabetic retinopathy, neuropathy, nephropathy, and cardiovascular diseases have been considered as the most common complications of diabetes. PRISMA guidelines will be used to conduct the systematic review. Further, the proposed study will be strengthened by selecting credible journal repositories, optimising the search query, utilising inclusion and exclusion criteria, and determining top-ranked journals. The paper presents data analysis design with feature subsets to predict the aforementioned four complications of diabetes, which is directly beneficial for clinicians, researchers, and model developers to assist their clinical decisions, research purposes, and feature selection phase in model designing respectively.

References

[1]
“International Diabetes Federation” (2019). IDF Diabetes Atlas
[2]
“DCCT/EDIC Research Group” (2016). Coprogression of cardiovascular risk factors in type 1 diabetes during 30 years of follow-up in the DCCT/EDIC study. Diabetes care, 39(9), 1621-1630.
[3]
Graff, R. E., Sanchez, A., Tobias, D. K., Rodríguez, D., Barrisford, G. W., Blute, M. L., (2018). Type 2 diabetes in relation to the risk of renal cell carcinoma among men and women in two large prospective cohort studies. Diabetes Care, 41(7), 1432-1437.
[4]
Andersen, S. T., Witte, D. R., Andersen, H., Bjerg, L., Bruun, N. H., Jørgensen, M. E., (2018). Risk-factor trajectories preceding diabetic polyneuropathy: ADDITION-Denmark. Diabetes Care, 41(9), 1955-1962.
[5]
Hippisley-Cox, J., Coupland, C., & Brindle, P. (2017). Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. bmj, 357, j2099.
[6]
Braffett, B. H., Gubitosi-Klug, R. A., Albers, J. W., Feldman, E. L., Martin, C. L., White, N. H., (2020). Risk factors for diabetic peripheral neuropathy and cardiovascular autonomic neuropathy in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. Diabetes, 69(5), 1000-1010.
[7]
Andersen, S. T., Witte, D. R., Dalsgaard, E.-M., Andersen, H., Nawroth, P., Fleming, T., (2018). Risk factors for incident diabetic polyneuropathy in a cohort with screen-detected type 2 diabetes followed for 13 years: ADDITION-Denmark. Diabetes Care, 41(5), 1068-1075.
[8]
Ehrenstein V, Kharrazi H, Lehmann H, & Taylor C O. (2019). Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User's Guide. In L. M. Gliklich RE, Dreyer NA, editors (Ed.), Obtaining Data From Electronic Health Records
[9]
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The, P. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine, 6(7), e1000097.
[10]
Pati, D., & Lorusso, L. N. (2018). How to Write a Systematic Review of the Literature. Herd, 11(1), 15-30.
[11]
Gopalakrishnan, S., & Ganeshkumar, P. (2013). Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare. Journal of family medicine and primary care, 2(1), 9-14.
[12]
Zhu, L., Cao, H., Zhang, T., Shen, H., Dong, W., Wang, L., (2016). The effect of diabetes mellitus on lung cancer prognosis: a PRISMA-compliant meta-analysis of cohort studies. Medicine, 95(17)
[13]
Silva, I., Almeida, J., & Vasconcelos, C. (2015). A PRISMA-driven systematic review for predictive risk factors of digital ulcers in systemic sclerosis patients. Autoimmunity Reviews, 14(2), 140-152.
[14]
Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel, Switzerland), 6(2), 54.
[15]
Kruse, C. S., Kothman, K., Anerobi, K., & Abanaka, L. (2016). Adoption factors of the electronic health record: a systematic review. JMIR medical informatics, 4(2), e19.
[16]
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. Faseb j, 22(2), 338-342.
[17]
Scells, H., & Zuccon, G. (2018). Generating better queries for systematic reviews The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 475-484):
[18]
Meline, T. (2006). Selecting studies for systemic review: Inclusion and exclusion criteria. Contemporary issues in communication science and disorders, 33(Spring), 21-27.
[19]
Beall, J. (2016). Best practices for scholarly authors in the age of predatory journals. The Annals of The Royal College of Surgeons of England, 98(2), 77-79.
[20]
Evans, R. S. (2016). Electronic Health Records: Then, Now, and in the Future. Yearbook of medical informatics, Suppl 1(Suppl 1), S48-S61.

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  • (2024)Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection StrategyApplied Artificial Intelligence10.1080/08839514.2024.233191938:1Online publication date: Apr-2024
  • (2024)Blockchain for sustainabilityTelecommunications Policy10.1016/j.telpol.2023.10267648:2Online publication date: 25-Jun-2024
  1. A Systematic Review on Extracting Predictors for Forecasting Complications of Diabetes Mellitus

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    ICMHI '21: Proceedings of the 5th International Conference on Medical and Health Informatics
    May 2021
    347 pages
    ISBN:9781450389846
    DOI:10.1145/3472813
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    Published: 26 October 2021

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    Author Tags

    1. Additional Keywords and Phrases Diabetes mellitus
    2. Complications of diabetes
    3. PRISMA
    4. Risk factors
    5. Systematic review

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    • (2024)Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection StrategyApplied Artificial Intelligence10.1080/08839514.2024.233191938:1Online publication date: Apr-2024
    • (2024)Blockchain for sustainabilityTelecommunications Policy10.1016/j.telpol.2023.10267648:2Online publication date: 25-Jun-2024

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