Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis

Jelinek, Herbert F.; Stranieri, Andrew; Yatsko, Andrew; Venkatraman, Sitalakshmi

doi:10.1016/j.compbiomed.2016.05.005

Cited by 26 publications

(20 citation statements)

References 29 publications

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“…The first category deals with biomarker discovery, which is a task mainly performed through feature selection techniques [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. Following a feature selection step, a classification algorithm is employed to assess the prediction accuracy of the selected features.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…In [31], authors combined gas chromatography–mass spectrometry (GC/MS) profiling with Random Forest, in an effort to explore relationships between 5′-AMP-activated protein kinase AMPK and DM. Jelinek et al [24] investigated whether additional biomarkers could be used together with HbA1c to improve diagnostic accuracy in T2D, in case HbA1c levels are below or equal to the current cut-off of 6.5%. They concluded that both 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, and interleukin-6 (IL-6) improved classification accuracy.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

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Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

970

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

970

530

View full text Add to dashboard Cite

show abstract

“…Compared to single HbA1c cut-off standard, this method improves diagnostic accuracy as well as provides a new idea for diagnosing diabetes. Paying attention to the relationship between HbA1c and oxidative stress markers, inflammatory cytokines and so on, they drew the conclusion that both 8hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, and interleukin-6 (IL-6) improved classification accuracy [22].…”

Section: Decision Treementioning

confidence: 99%

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Sun

Zhang

2019

Teh. vjesn.

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Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.

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“…Ranker method (RM) used for feature selection. Herbert et al [6] concluded that HbA1c (glycated haemoglobin) is one of diabetes causing factor. Farhi et al [7] used data mining to retrieve novel knowledge which was not known before.…”

Section: Related Workmentioning

confidence: 99%

Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

Alehegn¹,

Joshi²

2019

IJEAT

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In 21th century, IT plays a very important and helpful role in health care industries acting as a savior to human life. Data mining and machine learning are two sides of healthcare-IT. Proposed system considers one of the most common chronic diseases called diabetes. India and almost all other countries are worried about diabetic patients, so diabetes can termed as a global chronic disease. In this paper, well-known predictive machine learning techniques viz. SVM, Random Tree and ANN are applied on PIMA dataset. Results of SVM, ANN, and RT are 90.1%, 88.02%, and 83.59% respectively

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Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis

Cited by 26 publications

References 29 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Type II Diabetes Prediction Using Combo of SVM ANN and Random Tree

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