Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis,O. Tsave,Athanasios Salifoglou,Nicos Maglaveras,Ioannis Vlahavas,Ioanna Chouvarda +5 more
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Comparing different supervised machine learning algorithms for disease prediction
TL;DR: It is found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies), however, the Random Forest algorithm showed superior accuracy comparatively.
Journal ArticleDOI
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
TL;DR: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice.
Journal ArticleDOI
Predicting Diabetes Mellitus With Machine Learning Techniques.
TL;DR: The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used and principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) was used to reduce the dimensionality.
References
More filters
Journal ArticleDOI
Theoretical and Empirical Analysis of ReliefF and RReliefF
TL;DR: How and why Relief algorithms work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.
Journal ArticleDOI
Sure independence screening for ultrahigh dimensional feature space
Jianqing Fan,Jinchi Lv +1 more
TL;DR: In this article, the authors introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size.
Posted Content
Sure Independence Screening for Ultra-High Dimensional Feature Space
Jianqing Fan,Jinchi Lv +1 more
TL;DR: The concept of sure screening is introduced and a sure screening method that is based on correlation learning, called sure independence screening, is proposed to reduce dimensionality from high to a moderate scale that is below the sample size.
Journal ArticleDOI
Personalized Nutrition by Prediction of Glycemic Responses
David Zeevi,Tal Korem,Niv Zmora,Niv Zmora,David Israeli,Daphna Rothschild,Adina Weinberger,Orly Ben-Yacov,Dar Lador,Tali Avnit-Sagi,Maya Lotan-Pompan,Jotham Suez,Jemal Ali Mahdi,Elad Matot,Gal Malka,Noa Kosower,Michal Rein,Gili Zilberman-Schapira,Lenka Dohnalová,Meirav Pevsner-Fischer,Rony Bikovsky,Zamir Halpern,Eran Elinav,Eran Segal +23 more
TL;DR: A machine-learning algorithm is devised that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in an 800-person cohort and shows that it accurately predicts personalized postprandial glycemic response to real-life meals, and a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postpr andial responses and consistent alterations to gut microbiota configuration.
Book
Hypoglycemia in Diabetes
TL;DR: Pending the prevention and cure of diabetes or the development of methods that provide glucose-regulated insulin replacement or secretion, the authors need to learn to replace insulin in a much more physiological fashion, to prevent, correct, or compensate for compromised glucose counterregulation, or both if they are to achieve near-euglycemia safely in most people with diabetes.