Enhancing Chronic Kidney Disease Diagnosis with an Optimized Fuzzy Deep Neural Network: A Polycystic Kidney Disease Perspective


  • G. Gokila Deepa Assistant professor, Department of Artificial Intelligence and Data Science, PPG Institute of Technology, Coimbatore 641035, Tamil Nadu
  • B. Dhiyanesh Associate Professor, Department of Computer Science and Engineering, Dr.N.G.P Institute of Technology, Coimbatore 641048, Tamil Nadu
  • C. B. Selva Lakshmi Assistant Professor, Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai 625009, Tamil Nadu
  • T. Yawanikha Assistant professor, Department of Information Technology, Karpagam Institute of Technology, Coimbatore 641021, Tamil Nadu
  • I. Janani Assistant professor, Department of Information Technology, Sona college of Technology, Salem 636005, Tamil Nadu.
  • R. Radha Assistant professor, Department of Electrical and Electronics Engineering, Study World College of Engineering, Coimbatore 641105, Tamil Nadu.


Fuzzy, Neural Network, Chi-squared, Information Gain, Deep Learning, , Kidney Disease


In recent study, there has been significant interest in developing more effective diagnostics, treatments, and preventive measures to control Chronic Kidney Disease (CKD). This leads to millions of deaths due to increasingly inadequate, untimely, and expensive treatment methods. Kidney function decline affects millions worldwide annually. By constantly affecting kidney function, when both kidneys are damaged, the body’s overall health can be negatively impacted. Furthermore, chronic kidney disease impairs the kidneys more than other conditions. Individuals with CKD can live longer even with the disease. However, it may lead to severe medical complications, such as reduced kidney function and kidney damage. To overcome this problem, an Optimized Fuzzy Deep Neural Network (OFDNN) classifier can detect and predict polycystic or non-polycystic kidney disease. Then, we gathered the necessary dataset for Polycystic Kidney Disease (PKD) from Kaggle. Then, a pre-processing step can be applied to ensure satisfactory accuracy of the missing values. As a result, we can use Efficient Multi-Head Self-Focusing Based on Feature Weight (EMS-FW) methods to obtain the average total loss value in the decoder. Next, a feature selection method that relies on Chi-squared based on Mutual Information Gain (Chi2-MIG) can calculate predictive ability by classifying the dependent variable and removing redundant features. Finally, an improved(DL) model based on the OFDNN classifier is proposed to detect and diagnose polycystic or non-polycystic kidney disease. This method suggests that developing DL with predictive modeling is a promising approach to finding effective solutions. Comparison of the proposed OFDNN approach with existing information classifiers shows improved precision, F-measure, and recall accuracy.


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How to Cite

Deepa, G. G. ., Dhiyanesh, B. ., Lakshmi, C. B. S. ., Yawanikha, T. ., Janani, I. ., & Radha, R. . (2024). Enhancing Chronic Kidney Disease Diagnosis with an Optimized Fuzzy Deep Neural Network: A Polycystic Kidney Disease Perspective. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 344–353. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5146



Research Article