Deep Learning for Diabetes: A Systematic Review

Zhu, Taiyu; Li, Kezhi; Herrero, Pau; Georgiou, Pantelis

doi:10.1109/jbhi.2020.3040225

Cited by 139 publications

(96 citation statements)

References 97 publications

(111 reference statements)

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“…There are numerous related studies; for instance, references [25][26][27] focused on the use of artificial intelligence in detecting diabetes; however, these studies mainly focused on software and used hospital data. Other studies [28][29][30] combined software and hardware, as did our study and were related to diabetes; however, they focused only on diabetic retinopathy, which is a complication of diabetes.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Xiang

Shujin

Hong-fang³

et al. 2021

BioMed Research International

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In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Xiang

Shujin

Hong-fang³

et al. 2021

BioMed Research International

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“…The four databases used are interfaces that do not require institutional subscriptions unlike other databases such as Ovid, Scopus and Web of Science. The purpose of using open-access databases is to enhance the reproducibility of the search results (16). The last search performed was on 12th July 2021.…”

Section: Methodsmentioning

confidence: 99%

“…Several systematic reviews have examined the use of AI in diabetes. Zhu et al investigated the use of DL in DM, searching multiple databases and obtaining 40 studies (16). A total of 107 peerreviewed research articles were included in a study by Jyotismita Chaki et al on the use of AI in the detection and self-management of diabetic patients (17).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

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Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review

Abrar

2021

Preprint

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Background: Diabetic nephropathy (DN) is a serious microvascular complication that affects 40% of diabetes patients. In the last decade, artificial intelligence (AI) has been widely used in both structured and unstructured clinical data to improve the treatment of patients/potential patients with DN. Methods: This systematic review aims to cover all applications of AI in the clinical use of DN or related topics. Studies were searched in four open-access databases (Pubmed, IEEE Xplore, DBLP Computer Science Bibliography, and ACM digital library). Finally, the author manually searched the reference list of included studies in the study for additional relevant articles. Results: Finally, a total of 24 original peers reviewed articles were included in this study. Through a manual data extraction, the summary of key information such as applied AI algorithm, main outcomes, performance evaluation etc. was taken. Then the included studies underwent a quality assessment criterion, assessing the reproducibility, generalizability etc. Most of the included studies revealed that the AI frameworks outperformed conventional statistical methods. A summary of the limitations, such as lack of data availability or external validation of the framework, in the included studies, was also included. Conclusion: The rapid advancement of the AI framework and the exponential data generation in healthcare can be utilized and applied in clinical practices. The aid of AI can be instrumental in the treatment of DN.

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“…The rapid development of deep learning techniques and the availability of data, it is possible to predict diabetes related task: Diabetes diagnosis, management of glucose levels, and evaluation of complications related to diabetes. Diabetes detection is routinely aided by modern deep learning algorithms and frameworks [5].…”

Section: Introductionmentioning

confidence: 99%

A Comparative analysis of Early Stage Diabetes Prediction using Machine Learning and Deep learning Approach

Refat¹,

Amin²,

Kaushal³

et al. 2021

Preprint

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Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0$\%$, while the rest of the algorithms were over 90.0$\%$ accurate.<br>

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Deep Learning for Diabetes: A Systematic Review

Cited by 139 publications

References 97 publications

Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Artificial Intelligence‐Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review

A Comparative analysis of Early Stage Diabetes Prediction using Machine Learning and Deep learning Approach

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