Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learnin...Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learning models in which these factors can be utilized to predict the risk of cataract.The data used herein was collected by a Chinese physical examination center located in Shanghai.It contains more than 120,000 examinees and about 500 physical examination metrics.Firstly,association rules were adopted to filter 39 abnormalities which are more likely to incur the risk of cataract,and the significance of these abnormalities was tested with univariate analysis and multivariate analysis.The test results indicate that age,diabetes,refractive error,retinal arteriosclerosis,thyroid nodules,and incomplete mammary gland degeneration significantly increase the possibility of cataract.Various machine learning models were compared in terms of their performance in predicting the risk of cataract based on these six factors,among which the logistic regression model and the decision-tree based ensemble methods outperform others.The test set A U C of these models can reach 0.84.展开更多
Objectives:This study aimed to describe cardiovascular risk and cardiovascular disease(CVD)knowledge among older adults,and further explore the association between knowledge and risk.Methods:In this cross-sectional st...Objectives:This study aimed to describe cardiovascular risk and cardiovascular disease(CVD)knowledge among older adults,and further explore the association between knowledge and risk.Methods:In this cross-sectional study,we enrolled 1120 older adults who received physical examination in health centers.The participants were interviewed to obtain their behavioral risk factors related to CVD and clinical characteristics.A risk prediction chart was used to predict participants'cardiovascular risk based on clinical characteristics and behavioral risk factors.Participants'CVD knowledge was collected with a pretested knowledge questionnaire.Results:Among the 1120 participants,240(21.4%)had low cardiovascular risk,353(31.5%)had moderate cardiovascular risk,527(47%)had high and very high cardiovascular risk.The knowledge level about CVD among 0.8%of the 1120 participants was good while that of 56.9%was poor.Lower CVD knowledge level,older age,lower income,and lower educational level were the independent factors of higher cardiovascular risk level.Conclusions:This study highlights the need to reduce the cardiovascular risk among older adults.CVD knowledge should be considered when developing health interventions.展开更多
基金the National Key R&D Program of China under Grant No.2020AAA0103800.
文摘Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learning models in which these factors can be utilized to predict the risk of cataract.The data used herein was collected by a Chinese physical examination center located in Shanghai.It contains more than 120,000 examinees and about 500 physical examination metrics.Firstly,association rules were adopted to filter 39 abnormalities which are more likely to incur the risk of cataract,and the significance of these abnormalities was tested with univariate analysis and multivariate analysis.The test results indicate that age,diabetes,refractive error,retinal arteriosclerosis,thyroid nodules,and incomplete mammary gland degeneration significantly increase the possibility of cataract.Various machine learning models were compared in terms of their performance in predicting the risk of cataract based on these six factors,among which the logistic regression model and the decision-tree based ensemble methods outperform others.The test set A U C of these models can reach 0.84.
基金The study was funded by a grant from the National Natural Science Foundation of China(NSFC,contract grant number:81641112)Hunan Excellent Young Teachers Fund(contract grant number:2018191RQG010).
文摘Objectives:This study aimed to describe cardiovascular risk and cardiovascular disease(CVD)knowledge among older adults,and further explore the association between knowledge and risk.Methods:In this cross-sectional study,we enrolled 1120 older adults who received physical examination in health centers.The participants were interviewed to obtain their behavioral risk factors related to CVD and clinical characteristics.A risk prediction chart was used to predict participants'cardiovascular risk based on clinical characteristics and behavioral risk factors.Participants'CVD knowledge was collected with a pretested knowledge questionnaire.Results:Among the 1120 participants,240(21.4%)had low cardiovascular risk,353(31.5%)had moderate cardiovascular risk,527(47%)had high and very high cardiovascular risk.The knowledge level about CVD among 0.8%of the 1120 participants was good while that of 56.9%was poor.Lower CVD knowledge level,older age,lower income,and lower educational level were the independent factors of higher cardiovascular risk level.Conclusions:This study highlights the need to reduce the cardiovascular risk among older adults.CVD knowledge should be considered when developing health interventions.
