Machine Learning and Data Mining Methods in Diabetes Research
By Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, N. Maglaveras, Ioannis P. Vlahavas, Ioanna Chouvarda. Computational and Structural Biotechnology Journal, 2017
1,239 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Information Management, Information Systems
View Paper: https://www.semanticscholar.org/paper/86d7ef18644c45f9417239907daeba1b61dc8185
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Citations in the corpus
- Predicting Diabetes Mellitus With Machine Learning Techniques (2018, methodology): “The advantage of this method is the whole samples in the dataset are trained and tested, which can avoid the higher variance (Refaeilzadeh et al., 2016; Kavakiotis et al., 2017).”
- Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment (2017, background): “Their processing by machine learning, datamining, and big data analytics [101,104], however, is sensitive to the reliability of data sources [101,104].”
- Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, positive): “Kavakiotis et al. (2017) conducted a systematic review regarding the machine learning applications, data mining techniques, and tools used in the diabetes field to showcase the prediction and diagnosis of diabetes, its complications, and genetic conditions and situation, including the physical…”
- Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “6 These techniques in diabetic studies can be helpful in symptoms recognition, and disease forecasting Technological advancements in AI need to more effective with large data sets in diabetes prediction [42] ML applications need to produce facts on big data mining of medical data sets [42,43].”
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (2018, background): “The literature offers ample evidence of the use of artificial intelligence methods in the field of diabetes, such as in general surveys [4,5] or in particular domains, for example early diagnosis [6].”