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Current Diabetes Reviews

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ISSN (Online): 1875-6417

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Systematic Review Article

Comprehensive Factors for Predicting the Complications of Diabetes Mellitus: A Systematic Review

Author(s): Madurapperumage Anuradha Erandathi, William Yu Chung Wang*, Michael Mayo and Ching-Chi Lee

Volume 20, Issue 9, 2024

Published on: 04 January, 2024

Article ID: e040124225240 Pages: 17

DOI: 10.2174/0115733998271863231116062601

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Abstract

Background: This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases are considered in the study.

Method: The features used to predict the complications are identified and categorised by scrutinising the standards of electronic health records.

Result: Overall, 102 research articles have been reviewed, resulting in 59 frequent features being identified. Nineteen attributes are recognised as a standard in all four considered complications, which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP, HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics models to predict the complications of diabetes mellitus is a vital and contemporary requirement. A widely accepted feature set is beneficial for benchmarking the risk factors of complications of diabetes.

Conclusion: This study is a thorough literature review to provide a clear state of the art for academicians, clinicians, and other stakeholders regarding the risk factors and their importance.

Keywords: Diabetes mellitus, risk factors, machine learning, complications of diabetes, cholesterol, triglyceride, BMI.

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