G-04 PPT sem II.pptxG-04 PPT sem II.pptx

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1. ”Chronic Kidney Disease Prediction using Deep Learning” Group ID : G 04 Ms.Bankar Prachi Bhajandas Mr.Jadhav Anand Abhimanyu Mr.Thite Onkar Abhaykumar Mr.Vetal Shubham Maruti PRN NO: 72210002H PRN No: 72155514E PRN No: 72155587L PRN No: 72155589G Under the Guidance of Dr.A.B.Gavali S. B. Patil College of Engineering, Indapur-Pune Department of Computer Eng ineering Savitribai Phule Pune University, Pune Academic Year 2023-24.

2. Outline Introduction Literature Survey of Existing System Problem Statement Overview of Proposed System Hardware and Software Requirement Mathematical Model Algorithm UML Diagrams Results Advantages Applications Conclusion References for further reading Paper Publication Details

3. Introduction Chronic Kidney disease is a severe lifelong condition caused either by renal disease or by impaired functions of the kidneys. In the present area of research, Kidney cancer is one of the deadliest and crucial importance for the survival of the patient's diagnosis and classification. Early diagnosis and proper therapy can stop or delay the development of this chronic disease into the final stage where dialysis or renal transplantation is the only way of saving the life of the patient. The development of automated tools to accurately identify subtypes of kidney cancer is, therefore, an urgent challenge in the recent past. Treatment for chronic kidney disease focuses on slowing the progression of the kidney damage, usually by controlling the underlying cause.

4. Literature Survey of Existing System

5. Literature Survey of Existing System

6. Literature Survey of Existing System

7. Problem Statement Predicting if the kidney cancer disease is cancer or non cancer based on several observations.

8. Overview of Proposed System Figure: Architecture

9. Hardware and Software Requirement Software Requirement Operating System - Windows Application Server - Apache Tomcat Front End - HTML, Bootstrap, CSS Language - Python. Database - Mysql IDE - Pycharm,Visual Studio Hardware Requirement Processor - Intel i3/i5/i7 Speed - 3.1 GHz RAM - 4GB(min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA

10. Mathematical Model Input: ➤ Medical report ➤ Blood Creatinine ➤ Glominun Filtration Rate ➤ CMD loss report Output: ➤ Patient report positive or negative. ➤ Some suggestions related to patient report.

11. Algorithm Fusion Algorithm (SVM) Fusion algorithms, is the techniques used to combine information from multiple sources or modalities to make a more informed decision. Deep Neural Networks (DNN) A deep neural network (DNN) is a type of artificial neural network (ANN) with multiple layers between the input and output layers. CNN Algorithms Convolutional Neural Networks (CNNs) are primarily used for image- related tasks due to their ability to automatically learn hierarchical features from visual data

12. UML Diagram Figure: Use Case Diagram

13. UML Diagram Figure: Data Flow Diagram

14. UML Diagram Figure: Class Diagram

15. UML Diagram Figure: Activity Diagram

16. Results Figure: GUI Implementation:1

17. Results Figure: GUI Implementation:2

18. Results Figure: GUI Implementation:3

19. Advantages High Accuracy: They can learn complex patterns in data, leading to better predictive performance. Feature Learning: Deep learning models can automatically extract relevant features from raw data, reducing the need for manual feature engineering. Early Detection: Deep learning models can help in early detection of CKD by identifying subtle patterns in patient data that may not be obvious to human observers. Reduced Human Bias: Deep learning models can help reduce human bias in diagnosis.

20. Applications Risk Stratification: Deep learning can stratify CKD patients into risk categories, allowing for personalized treatment plans and resource allocation. Deep learning models can predict a patient’s response to different medications, helping healthcare providers choose the most effective and safe treatment options. These models can assist in optimizing medication regimens for CKD patients, reducing the risk of adverse drug reactions.

21. Conclusion The main goal of this is to use CNN model for the prediction of kidney disease to high degree of accuracy. We succeeded in classifying kidney disease dataset into CKD and non-CKD with best overall accuracy when the model was tested with a set of data that were not used during the training process. This is also highlighted the importance of the features used in the prediction of kidney disease.

22. References Shivadekar, S., Kataria, B., Limkar, S. et al. Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08709-5. Kale, R., Shirkande, S. T., Pawar, R., Chitre, A., Deokate, S. T., Rajput, S. D., Kumar, J. R. R. (2023). CR System with Efficient Spectrum Sensing and Optimized Handoff Latency to Get Best Quality of Service. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 829-839. Khetani, V., Gandhi, Y., Bhattacharya, S., Ajani, S. N., Limkar, S. (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253-262.

23. References Thatikonda, R., Vaddadi, S.A., Arnepalli, P.R.R. et al. Securing biomedical databases based on fuzzy method through blockchain technology. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08355-x. Vaddadi, S., Arnepalli, P. R., Thatikonda, R., Padthe, A. (2022). Effective malware detection approach based on deep learning in Cyber-Physical Systems. International Journal of Computer Science and Information Technology, 14(6), 01-12. Gaikwad, Yogesh J. "A Review on Self Learning based Methods for Real World Single Image Super Resolution." (2021).

24. References Rashmi, R. Patil, et al. "Rdpc: Secure cloud storage with deduplication technique." 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC). IEEE, 2020. Nagtilak, S., Rai, S., Kale, R. (2020). Internet of things: A survey on distributed attack detection using deep learning approach. In Proceeding of International Conference on Computational Science and Applications: ICCSA 2019 (pp. 157-165). Springer Singapore. Khetani, V., Nicholas, J., Bongirwar, A., Yeole, A. (2014). Securing web accounts using graphical password authentication through watermarking. International Journal of Computer Trends and Technology, 9(6), 269-274.

25. Paper Publication Details Figure: Paper Publication Details

26. Thank You..!