Effect of bariatric surgery on diabetic nephropathy in obese type 2 diabetes patients in a local retrospective 2 year study

Chao, Anthony; Sum, Chee Fang; Lam, Benjamin Chih Chiang; Cheng, Anton; Low, Sui Pheng; Lim, Su Chi

doi:10.1016/s0168-8227(16)31435-8

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have made many explorations in the field of diabetes diagnosis and treatment and have achieved some results [12][13][14][15][16][17][18][19][20][21]. By monitoring 16 patients, Abraham of the University of California used statistical methods to analyze the risk factors of diabetes and found that if the glycosylated hemoglobin index of diabetic patients can be obtained from 9.6% down to 7.2%, this can improve the physiological state of patients [22]. At the same time, some researchers used decision tree algorithm and multilayer perceptron to carry out comparative experiments [23].…”

Section: Introductionmentioning

confidence: 99%

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Wang

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network (“CNN+” for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the “CNN+” algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the “CNN+” model can get more reliable prediction results.

show abstract

Section: Introductionmentioning

confidence: 99%

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Wang

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

Effect of bariatric surgery on diabetic nephropathy in obese type 2 diabetes patients in a local retrospective 2 year study

Cited by 1 publication

References 0 publications

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network

Contact Info

Product

Resources

About