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Data Analysis and Knowledge Discovery
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Prediction of diabetic complications based on unbalanced data
Qiu Yunfei,Guo Lei
(School of Software, Liaoning Technical University, Huludao 125105, China)
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Abstract  

[Objective]In view of the problem of insufficient description ability of classifier and deviation of decision boundary caused by unbalanced data samples of diabetic complications,to explore a suitable classifier model,improve the prediction effect of diabetic complications.[Methods]At the data level,The improved smote oversampling algorithm (F_ Smote) changes the class distribution of unbalanced data;At the algorithm level,Balance accuracy, ROC and AUC under PR curve were used as evaluation indexes,Four single classifier learning models and four ensemble learning models are compared and analyzed.[Results]It was found that compared with the traditional over sampling algorithm, F_SMOTE oversampling algorithm improved the prediction value by 1.48% (accuracy), 4.14% (ROC) and 9.21% (PR) respectively;Compared with the single classifier learning model, the prediction value of ensemble learning model was improved by 9.78% (accuracy), 8.82% (ROC) and 45.9% (PR), respectively,the combination of F_ Smote algorithm and random forest (RF) model can reach 97.63% (accuracy), 98.97% (ROC) and 96.54% (PR) for unbalanced data.[Limitations]The time efficiency of model training needs to be further improved.[Conclusions]This method can not only provide multi angle analysis and prediction framework for data mining personnel, but also assist doctors in disease diagnosis and prevention.

Key words Unbalanced data      F_SMOTE algorithm      integrated learning      diabetic complications      
Published: 11 November 2020

Cite this article:

Qiu Yunfei, Guo Lei. Prediction of diabetic complications based on unbalanced data . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0353     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

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