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
Citations
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Proceedings ArticleDOI
Diabetes Prediction Using Machine Learning Analytics: Ensemble Learning Techniques
TL;DR: In this article , the authors proposed a model, named Diabetes Expert System using Machine Learning Analytics (DESMLA) to explore the diabetes data to predict the disease more effectively, the model uses the 5 most prominent oversampling techniques namely SMOTE, Borderline SMOTE and ADASYN SMOTE to get rid of this class imbalance problem of the diabetes dataset.
Proceedings ArticleDOI
Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach
TL;DR: In this paper , the decision tree algorithm was used for a predictive analysis of the PIMA Indian Diabetes dataset and the classification accuracies of 79.76% and 76.3% were obtained for balanced and unbalanced datasets respectively.
Book ChapterDOI
The Innovative Biomarkers and Machine Learning Approaches in Gestational Diabetes Mellitus (GDM): A Short Review
TL;DR: In this article, the authors investigated the recent hybrid approaches and their outcomes and tried to find possible research directions for efficient model development to predict GDM before its occurrence with the use of technology.
Journal ArticleDOI
Machine Learning Based Diabetes Detection Model for False Negative Reduction
TL;DR: In this paper , a machine learning model consisting of linear regression, logistic regression, k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) was proposed to predict diabetes.
Journal ArticleDOI
Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years’ Data
TL;DR: Improved performance with the accumulation of longitudinal data when using machine learning for diabetes prediction in MetS patients indicated that models based on longitudinal multiple years’ data may provide more personalized assessment tools for risk evaluation.
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