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Comparision based Prediction of Diabetic Nephropathy using Deep Learning Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 40 |
| Year of Publication: 2021 |
| Authors: Saxena Sachin Kumar, Shrivastava Jitendra Nath, Agarwal Gaurav |
10.5120/ijca2021921750
|
Saxena Sachin Kumar, Shrivastava Jitendra Nath, Agarwal Gaurav . Comparision based Prediction of Diabetic Nephropathy using Deep Learning Algorithm. International Journal of Computer Applications. 183, 40 ( Dec 2021), 7-13. DOI=10.5120/ijca2021921750
Abstract
India is the leading country in statistics in terms of mortality due to challenging hospital facilities and financial resources reach out to general people. Post COVID19, it has been researched that there is a big gap between traditional and recent medical facilities to patients inside hospitals all across the country. Furthermore, due to a lack of proper follow-ups and treatment of certain diseases like diabetes, millions of people are in extremis each day. This paper summarizes, the prediction of diabetic nephropathy of any patient using the deep learning image processing method namely VGG16 with 98 % accuracy. To accelerate the image training system design ROC and AUC curves have been defined also, to provide better results, optimal values have been compared with machine learning algorithms such as SVM, Random Forest, AdaBoost, etc. Patient images can be scanned digitally and the very first opinion can be obtained without expert knowledge acquisition. Dataset has been collected from Shri Ram Murti Smarak Hospital, Bareilly MRI, and Mission Hospital, Bareilly, Uttar Pradesh, India. General Term Deep Learning, Machine Learning
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