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
Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,...Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,the detection rate and BI-RADS classification of female breast nodules across China remain largely undocumented.Methods:This study analyzed health examination data from 6,412,893 urban women across 31 provincial-level administrative divisions(PLADs).We calculated detection rates of breast nodules and their various BI-RADS classifications.Chi-square(χ2)tests were performed to compare differences between groups.Multivariable logistic regression models were constructed to explore associations between breast nodules and BI-RADS 4-5 with demographic,socioeconomic,and metabolic indicators.Results:The overall detection rate of breast nodules in Chinese urban women was 27.9%,with provincial rates ranging from 11.6%to 37.0%.Among women with breast nodules marked with BI-RADS classification information,95.9%were categorized as BI-RADS 2-3,while 4.0%were classified as BI-RADS 4-5.Further analyses revealed that age,geographic region,per capita gross domestic product(GDP),body mass index(BMI),high triglyceride(TG),high lowdensity lipoprotein cholesterol(LDL-C),and diabetes were significant risk factors for BI-RADS 4-5 classification.Conclusions:This study highlights the importance of managing high-risk women with breast nodules through BI-RADS classification,underscoring the need for targeted health interventions while considering regional and socioeconomic disparities.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Strategic Research and Consultancy Project of the Chinese Academy of Engineering(2023-JB-11).
文摘Introduction:Female breast nodules represent the most frequently detected lesions during breast ultrasound screening.Notably,nodules classified as BIRADS 4 or 5 indicate an elevated risk of breast cancer.Nevertheless,the detection rate and BI-RADS classification of female breast nodules across China remain largely undocumented.Methods:This study analyzed health examination data from 6,412,893 urban women across 31 provincial-level administrative divisions(PLADs).We calculated detection rates of breast nodules and their various BI-RADS classifications.Chi-square(χ2)tests were performed to compare differences between groups.Multivariable logistic regression models were constructed to explore associations between breast nodules and BI-RADS 4-5 with demographic,socioeconomic,and metabolic indicators.Results:The overall detection rate of breast nodules in Chinese urban women was 27.9%,with provincial rates ranging from 11.6%to 37.0%.Among women with breast nodules marked with BI-RADS classification information,95.9%were categorized as BI-RADS 2-3,while 4.0%were classified as BI-RADS 4-5.Further analyses revealed that age,geographic region,per capita gross domestic product(GDP),body mass index(BMI),high triglyceride(TG),high lowdensity lipoprotein cholesterol(LDL-C),and diabetes were significant risk factors for BI-RADS 4-5 classification.Conclusions:This study highlights the importance of managing high-risk women with breast nodules through BI-RADS classification,underscoring the need for targeted health interventions while considering regional and socioeconomic disparities.
