FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022906464> ?p ?o ?g. }

• W2022906464 endingPage "726" @default.
• W2022906464 startingPage "722" @default.
• W2022906464 abstract "Hypertension (HTN) has been proven to be associated with an increased risk of cardiovascular diseases. The purpose of the study was to examine risk factors for HTN and to develop a prediction model to estimate HTN risk for rural residents over the age of 35 years. This study was based on a cross-sectional survey of 3054 rural community residents (N=3054). Participants were divided into two groups: a training set (N1=2438) and a validation set (N2=616). The differences between the training set and validation set were not statistically significant. The predictors of HTN risk were identified from the training set using logistic regression analysis. Some risk factors were significantly associated with HTN, such as a high educational level (EL) (odds ratio (OR)=0.744), a predominantly sedentary job (OR=1.090), a positive family history of HTN (OR=1.614), being overweight (OR=1.525), dysarteriotony (OR=1.101), alcohol intake (OR=0.760), a salty diet (OR=1.146), more vegetable and fruit intake (OR=0.882), meat consumption (OR=0.787) and regular physical exercise (OR=0.866). We established the predictive models using logistic regression model (LRM) and artificial neural network (ANN). The accuracy of the models was compared by receiver operating characteristic (ROC) when the models were applied to the validation set. The ANN model (area under the curve (AUC)=0.900±0.014) proved better than the LRM (AUC=0.732±0.026) in terms of evaluating the HTN risk because it had a larger area under the ROC curve." @default.
• W2022906464 created "2016-06-24" @default.
• W2022906464 creator A5003377806 @default.
• W2022906464 creator A5008046656 @default.
• W2022906464 creator A5009495506 @default.
• W2022906464 creator A5019253869 @default.
• W2022906464 creator A5030214508 @default.
• W2022906464 creator A5035258810 @default.
• W2022906464 creator A5051610166 @default.
• W2022906464 creator A5063481044 @default.
• W2022906464 date "2010-05-27" @default.
• W2022906464 modified "2023-10-12" @default.
• W2022906464 title "Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area" @default.
• W2022906464 cites W1950055884 @default.
• W2022906464 cites W1967388369 @default.
• W2022906464 cites W1985719600 @default.
• W2022906464 cites W1993604786 @default.
• W2022906464 cites W1993633402 @default.
• W2022906464 cites W2000228088 @default.
• W2022906464 cites W2015223144 @default.
• W2022906464 cites W2024462945 @default.
• W2022906464 cites W2027795929 @default.
• W2022906464 cites W2034042317 @default.
• W2022906464 cites W2034183411 @default.
• W2022906464 cites W2038623844 @default.
• W2022906464 cites W2042794875 @default.
• W2022906464 cites W2046529736 @default.
• W2022906464 cites W2048374514 @default.
• W2022906464 cites W2075453387 @default.
• W2022906464 cites W2088910937 @default.
• W2022906464 cites W2090578519 @default.
• W2022906464 cites W2091606388 @default.
• W2022906464 cites W2100710028 @default.
• W2022906464 cites W2114741955 @default.
• W2022906464 cites W2125577664 @default.
• W2022906464 cites W2140812062 @default.
• W2022906464 cites W2144233292 @default.
• W2022906464 cites W2146115904 @default.
• W2022906464 cites W2154188964 @default.
• W2022906464 cites W2166185432 @default.
• W2022906464 cites W2605404585 @default.
• W2022906464 cites W2738296429 @default.
• W2022906464 cites W4235312365 @default.
• W2022906464 cites W4301971222 @default.
• W2022906464 cites W75245760 @default.
• W2022906464 cites W78951109 @default.
• W2022906464 doi "https://doi.org/10.1038/hr.2010.73" @default.
• W2022906464 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20505678" @default.
• W2022906464 hasPublicationYear "2010" @default.
• W2022906464 type Work @default.
• W2022906464 sameAs 2022906464 @default.
• W2022906464 citedByCount "37" @default.
• W2022906464 countsByYear W20229064642013 @default.
• W2022906464 countsByYear W20229064642014 @default.
• W2022906464 countsByYear W20229064642016 @default.
• W2022906464 countsByYear W20229064642017 @default.
• W2022906464 countsByYear W20229064642018 @default.
• W2022906464 countsByYear W20229064642019 @default.
• W2022906464 countsByYear W20229064642020 @default.
• W2022906464 countsByYear W20229064642021 @default.
• W2022906464 countsByYear W20229064642022 @default.
• W2022906464 countsByYear W20229064642023 @default.
• W2022906464 crossrefType "journal-article" @default.
• W2022906464 hasAuthorship W2022906464A5003377806 @default.
• W2022906464 hasAuthorship W2022906464A5008046656 @default.
• W2022906464 hasAuthorship W2022906464A5009495506 @default.
• W2022906464 hasAuthorship W2022906464A5019253869 @default.
• W2022906464 hasAuthorship W2022906464A5030214508 @default.
• W2022906464 hasAuthorship W2022906464A5035258810 @default.
• W2022906464 hasAuthorship W2022906464A5051610166 @default.
• W2022906464 hasAuthorship W2022906464A5063481044 @default.
• W2022906464 hasBestOaLocation W20229064641 @default.
• W2022906464 hasConcept C11783203 @default.
• W2022906464 hasConcept C126322002 @default.
• W2022906464 hasConcept C143095724 @default.
• W2022906464 hasConcept C144024400 @default.
• W2022906464 hasConcept C149923435 @default.
• W2022906464 hasConcept C151956035 @default.
• W2022906464 hasConcept C156957248 @default.
• W2022906464 hasConcept C2779134260 @default.
• W2022906464 hasConcept C2780586474 @default.
• W2022906464 hasConcept C2781179581 @default.
• W2022906464 hasConcept C511355011 @default.
• W2022906464 hasConcept C58471807 @default.
• W2022906464 hasConcept C71924100 @default.
• W2022906464 hasConceptScore W2022906464C11783203 @default.
• W2022906464 hasConceptScore W2022906464C126322002 @default.
• W2022906464 hasConceptScore W2022906464C143095724 @default.
• W2022906464 hasConceptScore W2022906464C144024400 @default.
• W2022906464 hasConceptScore W2022906464C149923435 @default.
• W2022906464 hasConceptScore W2022906464C151956035 @default.
• W2022906464 hasConceptScore W2022906464C156957248 @default.
• W2022906464 hasConceptScore W2022906464C2779134260 @default.
• W2022906464 hasConceptScore W2022906464C2780586474 @default.
• W2022906464 hasConceptScore W2022906464C2781179581 @default.
• W2022906464 hasConceptScore W2022906464C511355011 @default.
• W2022906464 hasConceptScore W2022906464C58471807 @default.
• W2022906464 hasConceptScore W2022906464C71924100 @default.

• next