Additional file 9 of The automatic detection of diabetic kidney disease from...
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zipUpdated Aug 16, 2024
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EmailClick to copy linkLink copiedCiteShaomin Shi; Ling Gao; Juan Zhang; Baifang Zhang; Jing Xiao; Wan Xu; Yuan Tian; Lihua Ni; Xiaoyan Wu (2024). Additional file 9 of The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients [Dataset]. http://doi.org/10.6084/m9.figshare.26634081.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.26634081.v1Dataset updatedAug 16, 2024AuthorsShaomin Shi; Ling Gao; Juan Zhang; Baifang Zhang; Jing Xiao; Wan Xu; Yuan Tian; Lihua Ni; Xiaoyan WuLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionAdditional file 9. Supplementary Python source codes. Code1: Python code for the model using RF classifier with SMOTE correction for data set imbalance. Code2: Python code for the model using SVM classifier with SMOTE correction for data set imbalance. Code3: Python code for the model using BDT classifier with SMOTE correction for data set imbalance. Code4: Python code for the model using Ada classifier with SMOTE correction for data set imbalance. Code5: Python code for the model using RF classifier with Random oversampling correction for data set imbalance. Code6: Python code for the model using SVM classifier with Random oversampling correction for data set imbalance. Code7: Python code for the model using BDT classifier with Random oversampling correction for data set imbalance. Code8: Python code for the model using Ada classifier with Random oversampling correction for data set imbalance. Code9: Python code for the model using RF classifier with no correction for data set imbalance. Code10: Python code for the model using SVM classifier with no correction for data set imbalance. Code11: Python code for the model using BDT classifier with no correction for data set imbalance. Code12: Python code for the model using Ada classifier with no correction for data set imbalance. Code13: Python code for the ROC curves of models with SMOTE correction for data set imbalance. Code14: Python code for the ROC curves of models with Random oversampling correction for data set imbalance. Code15: Python code for the ROC curves of models with no correction for data set imbalance. Code16: Python code for the model using RF classifier with SMOTE correction for data set imbalance, and imputing the missing data by the method of backfilling missing values. Code17: Python code for the model using RF classifier with SMOTE correction for data set imbalance, and imputing the missing data by means. Code18: Python code for tunning of the model using RF classifier with SMOTE correction for data set imbalance. Code19: Python code for calculating the standard deviations.
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FacebookTwitterEmailAdditional file 9 of The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Additional file 9. Supplementary Python source codes. Code1: Python code for the model using RF classifier with SMOTE correction for data set imbalance. Code2: Python code for the model using SVM classifier with SMOTE correction for data set imbalance. Code3: Python code for the model using BDT classifier with SMOTE correction for data set imbalance. Code4: Python code for the model using Ada classifier with SMOTE correction for data set imbalance. Code5: Python code for the model using RF classifier with Random oversampling correction for data set imbalance. Code6: Python code for the model using SVM classifier with Random oversampling correction for data set imbalance. Code7: Python code for the model using BDT classifier with Random oversampling correction for data set imbalance. Code8: Python code for the model using Ada classifier with Random oversampling correction for data set imbalance. Code9: Python code for the model using RF classifier with no correction for data set imbalance. Code10: Python code for the model using SVM classifier with no correction for data set imbalance. Code11: Python code for the model using BDT classifier with no correction for data set imbalance. Code12: Python code for the model using Ada classifier with no correction for data set imbalance. Code13: Python code for the ROC curves of models with SMOTE correction for data set imbalance. Code14: Python code for the ROC curves of models with Random oversampling correction for data set imbalance. Code15: Python code for the ROC curves of models with no correction for data set imbalance. Code16: Python code for the model using RF classifier with SMOTE correction for data set imbalance, and imputing the missing data by the method of backfilling missing values. Code17: Python code for the model using RF classifier with SMOTE correction for data set imbalance, and imputing the missing data by means. Code18: Python code for tunning of the model using RF classifier with SMOTE correction for data set imbalance. Code19: Python code for calculating the standard deviations.