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Comparision based Prediction of Diabetic Nephropathy using Deep Learning Algorithm

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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 and Agarwal Gaurav. Comparision based Prediction of Diabetic Nephropathy using Deep Learning Algorithm. International Journal of Computer Applications 183(40):7-13, December 2021. BibTeX

@article{10.5120/ijca2021921750,
	author = {Saxena Sachin Kumar and Shrivastava Jitendra Nath and Agarwal Gaurav},
	title = {Comparision based Prediction of Diabetic Nephropathy using Deep Learning Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2021},
	volume = {183},
	number = {40},
	month = {Dec},
	year = {2021},
	issn = {0975-8887},
	pages = {7-13},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume183/number40/32193-2021921750},
	doi = {10.5120/ijca2021921750},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

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

References

  1. [Online]. Available: https://www.idf.org/news/225:idf-ncda-statement-wha-resolution.html.
  2. [Online]. Available: https://www.who.int/news/item/14-04-2021-new-who-global-compact-to-speed-up-action-to-tackle-diabetes.
  3. [Online]. Available: https://idf.org/our-network/regions-members/south-east-asia/diabetes-in-sea.html.
  4. [Online]. Available: https://www3.paho.org/hq/index.php?option=com_content&view=article&id=6717:2012-about-diabetes&Itemid=39447&lang=en.
  5. [Online]. Available: https://www.kidney.org/atoz/content/diabetes.
  6. [Online]. Available: https://docs.google.com/forms/d/e/1FAIpQLScX3kpkPyBPDyCndl2_hWl27qwrkaDDlGZVEGLWoeWokAWV0A/alreadyresponded.
  7. “Y. Pan, M. Fu, B. Cheng, X. Tao and J. Guo, ‘Enhanced Deep Learning As-sisted Convolutional Neural Network for Heart Disease Prediction on the In-ternet of Medical Things Platform,’ in IEEE Access, vol. 8, pp. 189503-189512, 2020, doi: 10.1109/ACCESS.2020.3026214.”
  8. “Z. KARHAN and F. AKAL, ‘Covid-19 Classification Using Deep Learning in Chest X-Ray Images,’ 2020 Medical Technologies Congress (TIPTEKNO), 2020, pp. 1-4, doi: 10.1109/TIPTEKNO50054.2020.9299315.”
  9. “J. C. Sangidong, H. D. Purnomo and F. Y. Santoso, ‘Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images,’ 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2021, pp. 61-65, doi: 10.1109/EIConCIT50028.2021.9431887.”
  10. “S. M. M. Elkholy, A. Rezk and A. A. E. F. Saleh, ‘Early Prediction of Chron-ic Kidney Disease using Deep Belief Network,’ in IEEE Access, doi: 10.1109/ACCESS.2021.3114306.”
  11. “H. Zhang, Y. Chen, Y. Song, Z. Xiong, Y. Yang and Q. M. Jonathan Wu, ‘Automatic Kidney Lesion Detection for CT Images Using Morphological Cascade Convolutional Neural Networks,’ in IEEE Access, vol. 7, pp. 83001-83011, 2019, doi: 10.1109/ACCESS.2019.2924207.”
  12. “G. Chen et al., ‘Prediction of Chronic Kidney Disease Using Adaptive Hy-bridized Deep Convolutional Neural Network on the Internet of Medical Things Platform,’ in IEEE Access, vol. 8, pp. 100497-100508, 2020, doi: 10.1109/ACCESS.2020.2995310.”
  13. [Online]. Available: https://www.who.int/health-topics/diabetes#tab=tab_1.
  14. “X. Yan, K. Yuan, W. Zhao, S. Wang, Z. Li and S. Cui, ‘An Efficient Hybrid Model for Kidney Tumor Segmentation in CT Images,’ 2020 IEEE 17th Inter-national Symposium on Biomedical Imaging (ISBI), 2020, pp. 333-336, doi: 10.1109/ISBI45749.2020.9098325.”
  15. “A. A. Neloy, S. Alam, R. A. Bindu and N. J. Moni, ‘Machine Learning based Health Prediction System using IBM Cloud as PaaS,’ 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 444-450, doi: 10.1109/ICOEI.2019.8862754.”
  16. “H. Dou, ‘Applications of Machine Learning in The Field of Medical Care,’ 2019 34rd Youth Academic Annual Conference of Chinese Association of Au-tomation (YAC), 2019, pp. 176-179, doi: 10.1109/YAC.2019.8787685.”
  17. “M. M. Baig et al., ‘Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge,’ 2019 41st An-nual International Conference of the IEEE Engineering in Medicine and Biolo-gy Society (EMBC), 2019, pp. 2178-2181, doi: 10.1109/EMBC.2019.8856646.”
  18. Kakde, A., Arora, N., & Sharma, D. (2019). A comparative study of different types of cnn and highway cnn techniques. Global Journal of Engineering Science and Research Management, 6(4), 18-31.
  19. Kakde, A., Arora, N., Sharma, D., & Sharma, S. C. (2020). Multi spectral classification and recognition of breast cancer and pneumonia. Polish Journal of Medical Physics and Engineering, 26(1), 1-9.
  20. Kakde, A., Sharma, D., Kaushik, B., & Arora, N. (2021). Investigation of Solar Flare Classification to Identify Optimal Performance. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 20(1), 28-41.
  21. Kakde, A., Arora, N., & Sharma, D. (2018). Fire Detection System Using Artificial Intelligence Techniques. International Journal of Research in Engineering, IT and Social Sciences, 8(11), 1-5.

Keywords

Deep learning, machine learning, medical care, diabetic nephropathy, VGG16