FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4307990043> ?p ?o ?g. }

• W4307990043 endingPage "14280" @default.
• W4307990043 startingPage "14280" @default.
• W4307990043 abstract "Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm's performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction." @default.
• W4307990043 created "2022-11-07" @default.
• W4307990043 creator A5025121848 @default.
• W4307990043 creator A5041948298 @default.
• W4307990043 creator A5049234362 @default.
• W4307990043 creator A5066437513 @default.
• W4307990043 date "2022-11-01" @default.
• W4307990043 modified "2023-10-09" @default.
• W4307990043 title "Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach" @default.
• W4307990043 cites W1549923419 @default.
• W4307990043 cites W1585367646 @default.
• W4307990043 cites W1615991656 @default.
• W4307990043 cites W1816688868 @default.
• W4307990043 cites W1912096016 @default.
• W4307990043 cites W1913815100 @default.
• W4307990043 cites W1987063664 @default.
• W4307990043 cites W2002455669 @default.
• W4307990043 cites W2020502126 @default.
• W4307990043 cites W2024067944 @default.
• W4307990043 cites W2027234849 @default.
• W4307990043 cites W2055191986 @default.
• W4307990043 cites W2068636224 @default.
• W4307990043 cites W2097453405 @default.
• W4307990043 cites W2103276278 @default.
• W4307990043 cites W2107328434 @default.
• W4307990043 cites W2112741060 @default.
• W4307990043 cites W2126930838 @default.
• W4307990043 cites W2130347794 @default.
• W4307990043 cites W2140716320 @default.
• W4307990043 cites W2142903999 @default.
• W4307990043 cites W2218047931 @default.
• W4307990043 cites W2247462025 @default.
• W4307990043 cites W2531360867 @default.
• W4307990043 cites W2557801006 @default.
• W4307990043 cites W2616911995 @default.
• W4307990043 cites W2659296870 @default.
• W4307990043 cites W2734626658 @default.
• W4307990043 cites W2736872388 @default.
• W4307990043 cites W2802667344 @default.
• W4307990043 cites W2885069035 @default.
• W4307990043 cites W2900329012 @default.
• W4307990043 cites W2917748145 @default.
• W4307990043 cites W2927235903 @default.
• W4307990043 cites W2945447024 @default.
• W4307990043 cites W2950722229 @default.
• W4307990043 cites W2981581709 @default.
• W4307990043 cites W3045445851 @default.
• W4307990043 cites W3083610965 @default.
• W4307990043 cites W3084116491 @default.
• W4307990043 cites W3086029573 @default.
• W4307990043 cites W3090507198 @default.
• W4307990043 cites W3135092421 @default.
• W4307990043 cites W3165166157 @default.
• W4307990043 cites W4246228692 @default.
• W4307990043 doi "https://doi.org/10.3390/ijerph192114280" @default.
• W4307990043 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36361161" @default.
• W4307990043 hasPublicationYear "2022" @default.
• W4307990043 type Work @default.
• W4307990043 citedByCount "5" @default.
• W4307990043 countsByYear W43079900432023 @default.
• W4307990043 crossrefType "journal-article" @default.
• W4307990043 hasAuthorship W4307990043A5025121848 @default.
• W4307990043 hasAuthorship W4307990043A5041948298 @default.
• W4307990043 hasAuthorship W4307990043A5049234362 @default.
• W4307990043 hasAuthorship W4307990043A5066437513 @default.
• W4307990043 hasBestOaLocation W43079900431 @default.
• W4307990043 hasConcept C105795698 @default.
• W4307990043 hasConcept C119857082 @default.
• W4307990043 hasConcept C126322002 @default.
• W4307990043 hasConcept C134018914 @default.
• W4307990043 hasConcept C154945302 @default.
• W4307990043 hasConcept C2910068830 @default.
• W4307990043 hasConcept C33923547 @default.
• W4307990043 hasConcept C41008148 @default.
• W4307990043 hasConcept C44249647 @default.
• W4307990043 hasConcept C45804977 @default.
• W4307990043 hasConcept C50644808 @default.
• W4307990043 hasConcept C555293320 @default.
• W4307990043 hasConcept C71924100 @default.
• W4307990043 hasConcept C95190672 @default.
• W4307990043 hasConceptScore W4307990043C105795698 @default.
• W4307990043 hasConceptScore W4307990043C119857082 @default.
• W4307990043 hasConceptScore W4307990043C126322002 @default.
• W4307990043 hasConceptScore W4307990043C134018914 @default.
• W4307990043 hasConceptScore W4307990043C154945302 @default.
• W4307990043 hasConceptScore W4307990043C2910068830 @default.
• W4307990043 hasConceptScore W4307990043C33923547 @default.
• W4307990043 hasConceptScore W4307990043C41008148 @default.
• W4307990043 hasConceptScore W4307990043C44249647 @default.
• W4307990043 hasConceptScore W4307990043C45804977 @default.
• W4307990043 hasConceptScore W4307990043C50644808 @default.
• W4307990043 hasConceptScore W4307990043C555293320 @default.
• W4307990043 hasConceptScore W4307990043C71924100 @default.
• W4307990043 hasConceptScore W4307990043C95190672 @default.
• W4307990043 hasIssue "21" @default.
• W4307990043 hasLocation W43079900431 @default.
• W4307990043 hasLocation W43079900432 @default.
• W4307990043 hasLocation W43079900433 @default.

• next