Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis,O. Tsave,Athanasios Salifoglou,Nicos Maglaveras,Ioannis Vlahavas,Ioanna Chouvarda +5 more
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TLDR
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.Abstract:
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.read more
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References
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Type 2 Diabetes Mellitus Trajectories and Associated Risks.
Wonsuk Oh,Era Kim,M. Regina Castro,Pedro J. Caraballo,Vipin Kumar,Michael Steinbach,György J. Simon +6 more
TL;DR: A novel method to observe trajectories directly and demonstrate the effectiveness of the proposed method by studying type 2 diabetes mellitus (T2DM) trajectories, which is a typical trajectory that most people follow.
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Artificial neural network‐based drug design for diabetes mellitus using flavonoids
Jagdish C. Patra,Boon Heng Chua +1 more
TL;DR: In this paper, the authors carried out quantitative structure-activity relationship analysis of flavones and flavonols (members of the flavonoid family) using artificial neural networks, and they were able to predict 10 potent compounds with strong AR inhibition (IC50 < 0.3 μM) and RS activity (IC25 < 1.0 μM).
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Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier
Sulaimon Ibrahim,Pradeep Chowriappa,Sumeet Dua,U. Rajendra Acharya,Kevin Noronha,Sulatha V. Bhandary,Hatwib Mugasa +6 more
TL;DR: A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes that produced more useful rules and a more effective classification system.
Journal ArticleDOI
An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus
TL;DR: A novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetic mechanism (IEM), which is applied to predict the occurrence of Type 2 diabetes mellitus (DM).
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Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers
Zsolt Török,Tunde Peto,Eva Csosz,Edit Tukacs,Ágnes Molnár,András Berta,József Tözsér,Andras Hajdu,Valeria Nagy,Balint Domokos,Adrienne Csutak +10 more
TL;DR: The combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.