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

Sambyal, Nitigya; Saini, Poonam; Syal, Rupali

doi:10.2174/1573399816666200511003357

Cited by 20Artificial Intelligence in HealthcareMachine Learning in HealthcareDiabetes Management and Research

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

Sambyal, Nitigya, Saini, Poonam, Syal, Rupali·2021·Current Diabetes Reviews

DOI 10.2174/1573399816666200511003357

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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.

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Generative AI models can detect early signs of nerve and kidney damage by analyzing patient biomarkers and longitudinal health data.
“Similar applications have been explored in predicting neuropathy and nephropathy, where generative AI models can detect early signs of nerve and kidney damage by analyzing patient biomarkers and longitudinal health data (78).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [78]

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Authors: Sambyal, N., Saini, P., & Syal, R.
Affiliations: Punjab Engineering College (IN)
Corresponding author: Sambyal, Nitigya
ORCID: Sambyal, Nitigya
Saini, Poonam
Venue: Current Diabetes Reviews
Publisher: Bentham Science Publishers Ltd.
Published: 2021-02
Volume: 17
Issue: 2
Pages: 143-155
ISSN: 1573-3998 (ISSN-L)
DOI: 10.2174/1573399816666200511003357
OpenAlex: W3022998446
Cited by: 19
References: 30
Citations by year: 2026: 1 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 2 · 2020: 1
FWCI: 0.46
Type: journal article
Language: en
Open access: closed
Locations: Current Diabetes Reviews (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords: type 2 diabetes, microvascular complications, diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, machine learning, deep learning, artificial intelligence in healthcare, prediction model
MeSH terms: Machine Learning, Artificial Intelligence, Diabetes Mellitus, Type 2, Humans

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

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