Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening.
Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to...
| Published in: | BioMed Research International pp. 1 - 15 |
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| Main Authors: | , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
3/29/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155998185&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155998185 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/29/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155998185 155998185 155998185 10.1155/2022/8114049 155998185 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening. aug: au: Cardozo, Glauco Pintarelli, Guilherme Brasil Andreis, Guilherme Rettore Lopes, Annelise Correa Wengerkievicz Marques, Jefferson Luiz Brum affil: Academic Department of Health and Services, Federal Institute of Santa Catarina, Florianopolis, SC 88020-300, Brazil sug: subj: Diabetes Mellitus Diagnosis Diagnosis, Laboratory Health Screening Methods Machine Learning Models, Statistical Evaluation Human Regression Support Vector Machine Neural Networks (Computer) Random Forest Glycated Hemoglobin Blood Prediabetic State Diagnosis Descriptive Statistics Sensitivity and Specificity Validity ab: Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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