Machine learning and data mining methods in diabetes research

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

doi:10.1016/j.csbj.2016.12.005

Cited by 1,445Artificial Intelligence in HealthcareMachine Learning in HealthcareData Mining and Machine Learning Applications

Machine learning and data mining methods in diabetes research

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Kavakiotis, Ioannis, Tsave, Olga, Salifoglou, Athanasios, Maglaveras, Nicos, Vlahavas, Ioannis, Chouvarda, Ioanna·2017·Computational and Structural Biotechnology Journal

DOI 10.1016/j.csbj.2016.12.005PMC PMC5257026

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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.

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Multi-omics machine learning is still at an early stage, yet it has been explored across many application areas.
“Although ML analysis of multi-omics is still in its embryonic stage, it has already been explored for a wide range of applications, as reported in recent reviews on brain diseases (Garali et al., 2018;Young et al., 2013), diabetes (Kavakiotis et al., 2017), cancers (Borad and LoRusso, 2017;Chaudhary et al., 2017;Wong et al., 2016) cardiovascular disease (Weng et al., 2017), medical imaging (Ericks”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Kavakiotis et al., 2017

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Authors: Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I.
Affiliations: Aristotle University of Thessaloniki (GR)
Corresponding author: Kavakiotis, Ioannis
ORCID: Kavakiotis, Ioannis
Tsave, Olga
Salifoglou, Athanasios
Maglaveras, Nicos
Vlahavas, Ioannis
Chouvarda, Ioanna
Venue: Computational and Structural Biotechnology Journal
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2017-01
Volume: 15
Pages: 104-116
ISSN: 2001-0370 (ISSN-L)
DOI: 10.1016/j.csbj.2016.12.005
PMC: PMC5257026
OpenAlex: W2569214105
Cited by: 1,445
References: 143
Citations by year: 2026: 110 · 2025: 174 · 2024: 158 · 2023: 193 · 2022: 208 · 2021: 192 · 2020: 173 · 2019: 134 · 2018: 69 · 2017: 22 · 2016: 7 · 2015: 4 · 2014: 1
FWCI: 21.14
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: European Commission (644906, HORIZON2020)
Horizon 2020 Framework Programme (644906)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: diabetes mellitus, machine learning, data mining, electronic health records, diabetes diagnosis, diabetes prediction, diabetes complications, genetic background, environmental factors, clinical data, support vector machine, association rule mining, supervised learning, clinical decision support

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Machine learning and data mining methods in diabetes research

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