FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313894953> ?p ?o ?g. }

• W4313894953 abstract "Chronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China's rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients' quality of life.From April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively.12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD.ML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate." @default.
• W4313894953 created "2023-01-10" @default.
• W4313894953 creator A5003517307 @default.
• W4313894953 creator A5016525537 @default.
• W4313894953 creator A5029566710 @default.
• W4313894953 creator A5032576592 @default.
• W4313894953 creator A5053438728 @default.
• W4313894953 creator A5055139554 @default.
• W4313894953 creator A5055820540 @default.
• W4313894953 creator A5069919632 @default.
• W4313894953 creator A5091113010 @default.
• W4313894953 date "2023-01-09" @default.
• W4313894953 modified "2023-10-03" @default.
• W4313894953 title "Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province" @default.
• W4313894953 cites W1976777533 @default.
• W4313894953 cites W2085458723 @default.
• W4313894953 cites W2110895523 @default.
• W4313894953 cites W2113760558 @default.
• W4313894953 cites W2125575764 @default.
• W4313894953 cites W2155287847 @default.
• W4313894953 cites W2340716803 @default.
• W4313894953 cites W2412276356 @default.
• W4313894953 cites W2605253636 @default.
• W4313894953 cites W2761181345 @default.
• W4313894953 cites W2884597820 @default.
• W4313894953 cites W2905936436 @default.
• W4313894953 cites W2940010972 @default.
• W4313894953 cites W2956251445 @default.
• W4313894953 cites W2968513998 @default.
• W4313894953 cites W2981311951 @default.
• W4313894953 cites W3011139747 @default.
• W4313894953 cites W3024182761 @default.
• W4313894953 cites W3028499805 @default.
• W4313894953 cites W3081032187 @default.
• W4313894953 cites W3083150398 @default.
• W4313894953 cites W3084083970 @default.
• W4313894953 cites W3101901277 @default.
• W4313894953 cites W3111872407 @default.
• W4313894953 cites W3114486788 @default.
• W4313894953 cites W3122398453 @default.
• W4313894953 cites W3132601888 @default.
• W4313894953 cites W3135751641 @default.
• W4313894953 cites W3137802132 @default.
• W4313894953 cites W3145812136 @default.
• W4313894953 cites W3160639137 @default.
• W4313894953 cites W3172720427 @default.
• W4313894953 cites W3175249343 @default.
• W4313894953 cites W3183687128 @default.
• W4313894953 cites W3184965946 @default.
• W4313894953 cites W3208998361 @default.
• W4313894953 cites W3209264851 @default.
• W4313894953 cites W3212502336 @default.
• W4313894953 cites W3212856965 @default.
• W4313894953 cites W3214696816 @default.
• W4313894953 cites W4234678377 @default.
• W4313894953 cites W4288074321 @default.
• W4313894953 doi "https://doi.org/10.3389/fmed.2022.930541" @default.
• W4313894953 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36698845" @default.
• W4313894953 hasPublicationYear "2023" @default.
• W4313894953 type Work @default.
• W4313894953 citedByCount "1" @default.
• W4313894953 countsByYear W43138949532023 @default.
• W4313894953 crossrefType "journal-article" @default.
• W4313894953 hasAuthorship W4313894953A5003517307 @default.
• W4313894953 hasAuthorship W4313894953A5016525537 @default.
• W4313894953 hasAuthorship W4313894953A5029566710 @default.
• W4313894953 hasAuthorship W4313894953A5032576592 @default.
• W4313894953 hasAuthorship W4313894953A5053438728 @default.
• W4313894953 hasAuthorship W4313894953A5055139554 @default.
• W4313894953 hasAuthorship W4313894953A5055820540 @default.
• W4313894953 hasAuthorship W4313894953A5069919632 @default.
• W4313894953 hasAuthorship W4313894953A5091113010 @default.
• W4313894953 hasBestOaLocation W43138949531 @default.
• W4313894953 hasConcept C126322002 @default.
• W4313894953 hasConcept C136764020 @default.
• W4313894953 hasConcept C154945302 @default.
• W4313894953 hasConcept C159641895 @default.
• W4313894953 hasConcept C169258074 @default.
• W4313894953 hasConcept C2524010 @default.
• W4313894953 hasConcept C2776174234 @default.
• W4313894953 hasConcept C2778653478 @default.
• W4313894953 hasConcept C2780306776 @default.
• W4313894953 hasConcept C33923547 @default.
• W4313894953 hasConcept C37616216 @default.
• W4313894953 hasConcept C41008148 @default.
• W4313894953 hasConcept C61511704 @default.
• W4313894953 hasConcept C71924100 @default.
• W4313894953 hasConceptScore W4313894953C126322002 @default.
• W4313894953 hasConceptScore W4313894953C136764020 @default.
• W4313894953 hasConceptScore W4313894953C154945302 @default.
• W4313894953 hasConceptScore W4313894953C159641895 @default.
• W4313894953 hasConceptScore W4313894953C169258074 @default.
• W4313894953 hasConceptScore W4313894953C2524010 @default.
• W4313894953 hasConceptScore W4313894953C2776174234 @default.
• W4313894953 hasConceptScore W4313894953C2778653478 @default.
• W4313894953 hasConceptScore W4313894953C2780306776 @default.
• W4313894953 hasConceptScore W4313894953C33923547 @default.
• W4313894953 hasConceptScore W4313894953C37616216 @default.
• W4313894953 hasConceptScore W4313894953C41008148 @default.
• W4313894953 hasConceptScore W4313894953C61511704 @default.

• next