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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Journal ArticleDOI
Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies:
Kushan De Silva,Joanne Enticott,Christopher Barton,Andrew Forbes,Sajal Kumar Saha,Rujuta Nikam +5 more
TL;DR: This work aims to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance.
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A deep learning model for identification of diabetes type 2 based on nucleotide signals
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Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with Machine Learning
TL;DR: Results show that the method can be successfully used to select features for diabetes classifier and improve its performance, which will provide support for clinicians to quickly identify diabetes.
Ensemble Learning on Diabetes Data Set and Early Diabetes Prediction
TL;DR: Predicting diabetes before it occurs is a problem complex to solve as the disease is basically a lifestyle disease which requires self-assessment of the patient and prevention of the disease at every step, so for reducing the uncertainty ensemble learning is used for prediction of diabetes risk in future as ensembleLearning is a collection of various machine learning models.
Proceedings ArticleDOI
Statistic Solution for Machine Learning to Analyze Heart Disease Data
TL;DR: A new system for disease prediction using machine learning prediction algorithms (LR, ANN and SVC) by utilizing an effective approach of ETL, OLAP and data mining is designed, which showed that the best machine learning algorithm is SVC with 92% accuracy for the risk prediction model.
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