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
Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review
TL;DR: It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D.
Book ChapterDOI
Bioinformatics Data Models, Representation and Storage
TL;DR: This work presents a set of available tools for biologic data management and algorithms for processing and knowledge extraction in biology-related problems.
Journal ArticleDOI
Data Mining and Endocrine Diseases: A New Way to Classify?
TL;DR: Data mining is used to predict, identify biomarkers, complications, therapies, health policies, genetic and environmental effects of this disease and could be generalized in the field of endocrinology, in the classification of other endocrine diseases.
Journal ArticleDOI
Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices
Victor Chang,Le Minh Thao Doan,Qianwen Ariel Xu,Karl Hall,Yuanyuan Anna Wang,M. Mustafa Kamal +5 more
TL;DR: In this paper , a framework integrating the traditional statistical and machine learning-based approach was proposed to analyze a large amount of data; and thereby facilitate a data-driven analytic model to manage omnichannel healthcare supply chain businesses.
Journal ArticleDOI
Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection
TL;DR: In this paper , a hybrid feature selection method for selecting the best features for detecting heart disease was introduced. And the best feature relevant to disease detection is selected through the proposed hybrid Congruence coefficient Kumar-Hassebrook similarity.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.