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Review Article

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

Author(s): Nitigya Sambyal*, Poonam Saini and Rupali Syal

Volume 17, Issue 2, 2021

Published on: 10 May, 2020

Page: [143 - 155] Pages: 13

DOI: 10.2174/1573399816666200511003357

Price: $65

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Abstract

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels, and nerves.

Methods: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications, mainly retinopathy, neuropathy, and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review.

Results: It has been observed that statistical analysis can help only in inferential and descriptive analysis whereas, AI-based machine learning models can even provide actionable prediction models for faster and accurate diagnosis of complications associated with DM.

Conclusion: The integration of AI-based analytics techniques, like machine learning and deep learning in clinical medicine, will result in improved disease management through faster disease detection and cost reduction for the treatment.

Keywords: Microvascular complications, retinopathy, nephropathy, neuropathy, machine learning, statistical analysis.

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