Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/14159
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dunja Vrbaški | en_US |
dc.contributor.author | Milan Vrbaški | en_US |
dc.contributor.author | Aleksandar Kupusinac | en_US |
dc.contributor.author | Darko Ivanović | en_US |
dc.contributor.author | Edita Stokić | en_US |
dc.contributor.author | Dragan Ivetić | en_US |
dc.contributor.author | Ksenija Doroslovački | en_US |
dc.date.accessioned | 2020-03-03T14:55:09Z | - |
dc.date.available | 2020-03-03T14:55:09Z | - |
dc.date.issued | 2019-11-01 | - |
dc.identifier.issn | 9333657 | en_US |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/14159 | - |
dc.description.abstract | © 2019 Elsevier B.V. Metabolic Syndrome (MetS) is associated with the risk of developing chronic disease (atherosclerotic cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease) and has an important role in early prevention. Previous research showed that an artificial neural network (ANN) is a suitable tool for algorithmic MetS diagnostics, that includes solely non-invasive, low-cost and easily-obtainabled (NI&LC&EO) diagnostic methods. This paper considers using four well-known machine learning methods (linear regression, artificial neural network, decision tree and random forest) for MetS predictions and provides their comparison, in order to induce and facilitate development of appropriate medical software by using these methods. Training, validation and testing are conducted on the large dataset that includes 3000 persons. Input vectors are very simple and contain the following parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures, while the output is MetS diagnosis in true/false form, made in accordance with International Diabetes Federation (IDF). Comparison leads to the conclusion that random forest achieves the highest specificity (SPC=0.9436), sensitivity (SNS=0.9154), positive (PPV=0.9379) and negative (NPV=0.9150) predictive values. Algorithmic diagnosis of MetS could be beneficial in everyday clinical practice since it can easily identify high risk patients. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Artificial Intelligence in Medicine | en_US |
dc.subject | Linear regression | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Decision tree | en_US |
dc.subject | Random forest | en_US |
dc.subject | Metabolic syndrome | en_US |
dc.title | Methods for algorithmic diagnosis of metabolic syndrome | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1016/j.artmed.2019.101708 | - |
dc.identifier.pmid | 101 | - |
dc.identifier.scopus | 2-s2.0-85073248157 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85073248157 | - |
dc.description.version | Published | en_US |
dc.relation.volume | 101 | en_US |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Fakultet tehničkih nauka, Departman za računarstvo i automatiku | - |
crisitem.author.dept | Fakultet tehničkih nauka, Departman za računarstvo i automatiku | - |
crisitem.author.dept | Medicinski fakultet, Katedra za internu medicinu | - |
crisitem.author.dept | Fakultet tehničkih nauka, Departman za računarstvo i automatiku | - |
crisitem.author.dept | Fakultet tehničkih nauka, Departman za opšte discipline u tehnici | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
crisitem.author.parentorg | Medicinski fakultet | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
crisitem.author.parentorg | Fakultet tehničkih nauka | - |
Appears in Collections: | FTN Publikacije/Publications |
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