Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models

Sambyal, Nitigya; Saini, Poonam; Syal, Rupali

doi:10.1007/s11277-020-07552-3

Cited by 17 publications

(13 citation statements)

References 33 publications

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“…Previous reviews have explored machine learning techniques in diabetes, yet with a substantially different focus. Sambyal et al conducted a review on microvascular complications in diabetes (retinopathy, neuropathy, nephropathy) [ 20 ]. This review included 31 studies classified into three groups according to the methods used: statistical techniques, machine learning, and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Fregoso-Aparicio

Noguez

Montesinos

et al. 2021

Diabetol Metab Syndr

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Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

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Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Fregoso-Aparicio

Noguez

Montesinos

et al. 2021

Diabetol Metab Syndr

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show abstract

“…In this manner, pre-trained machine learning models can make the process faster and less rigorous for healthcare suppliers and practitioners. Different sorts of machine learning algorithms, such as support vector machines (SVM), K-nearest neighbor (KNN), choice trees, etc., have been utilized broadly within the research associated with type 2 diabetes microvascular complications [66].…”

Section: Machine Learning As a Screening Toolmentioning

confidence: 99%

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

Rashid

Alkhodari

Mukit

et al. 2022

JCM

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Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ2) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin–creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.

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“…Different sorts of machine learning algorithms such as support vector machines (SVM), K-nearest neighbor (KNN), choice trees, etc. have been utilized broadly within the research associated with type-2 diabetes microvascular complications [66].…”

Section: Machine Learning As a Screening Toolmentioning

confidence: 99%

“…al. [66] provided a review of using machine learning models to classify diabetes microvascular complications in 2020. The authors showed that most of the work for classifying RET had been done using fundus image as the input and he compares the different achieved accuracy of different classifiers by different authors.…”

Section: Machine Learning As a Screening Toolmentioning

confidence: 99%

Machine Learning for Screening Microvascular Complications in Type-2 Diabetic Patients using Demographic, Clinical, and Laboratory Profiles

Rashid¹,

Alkhodari²,

Mukit³

et al. 2021

Preprint

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Microvascular complications are one of the key causes of mortality among type-2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications from patient demographic, clinical, and laboratory profiles only. A total of 96 Bangladeshi participants having type-2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a Chi-squared (2) test to statistically determine the most important markers in predicting four microvascular complications; namely cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), diabetic nephropathy (NEP), and diabetic retinopathy (RET). A machine learning approach based on random forest (RF) and support vector machine (SVM) was then developed to ensure automated clinical testing for microvascular complication in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, Albumin-Creatinine ratio, and gender for CAN testing (98.67%), Microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%), Albumin-Creatinine ratio for NEP testing (100%), and hemoglobin A1C, Microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prvent those patients from further microvascular complications leading to early death.

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Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models

Cited by 17 publications

References 33 publications

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

Machine Learning for Screening Microvascular Complications in Type-2 Diabetic Patients using Demographic, Clinical, and Laboratory Profiles

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