Author:
K. Jothimani, S.Thangamani, K.Ushmansherif, P. Manojkumar, S.GowrishankarPublished in
Journal of Science Technology and Research( Volume , Issue )
1.M. M. Hossain et al., “Mechanical anisotropy evaluation in kidney cortex utilizing ARFI top relocation: Preclinical approval and pilot in vivo clinical outcomes in kidney allografts,” IEEE Trans. Ultrason. Ferr., vol. 66, no. 3, pp. 551-562, Mar. 2019. 2.E. Hodneland et al., “In vivo identification of persistent kidney illness utilizing tissue distortion fields from dynamic MR imaging,” IEEE Trans. BioMed. Eng., vol. 66, no. 6, pp. 1779-1790, Jun. 2019.
3.G. R. Vasquez-Morales et al., “Logical forecast of ongoing renal illness in the colombian populace utilizing neural organizations and case-based thinking,” IEEE Access, vol. 7, pp. 152900-152910, Oct. 2019.
4.N. Almansour et al., “Neural organization and backing vector machine for the forecast of constant kidney illness: A relative report,” Comput. Biol. Drug., vol. 109, pp. 101-111, Jun. 2019
5.M. Alloghani et al., “Utilizations of AI strategies for programming learning and early expectation of understudies’ exhibition,” in Proc. Int. Conf. Delicate Computing in Data Science, Dec. 2018, pp. 246-258.
6.L. Du et al., “An AI based way to deal with distinguish safeguarded wellbeing data in Chinese clinical text,” Int. J. Drug. Illuminate., vol. 116, pp. 24-32, Aug. 2018
7.R. Abbas et al., “Characterization of fetal trouble and hypoxia utilizing AI draws near,” in Proc. Int. Conf. Insightful Computing, Jul. 2018, pp. 767-776
8.M. Mahyoub, M. Randles, T. Bread cook and P. Yang, “Examination investigation of AI calculations to rank alzheimer’s infection hazard factors by significance,” in Proc. eleventh Int. Conf. Improvements in eSystems Engineering, Sep. 2018.
9.Q. Zou et al., “Foreseeing diabetes mellitus with AI strategies,” Front. Genet., vol. 9, Nov. 2018
10.Z. Gao et al., “Determination of diabetic retinopathy utilizing profound neural organizations,” IEEE Access, vol. 7, pp. 3360-3370, Dec. 2018.
11. Hemalatha, E., Dhamodaran, M. and Punarselvam, E., 2019. Robust Data Collection with Multiple Sink Zone in 3-D Underwater Sensor Networks. International Journal on Applications in Basic and Applied Sciences, 5(1), pp.8-14.
12. Punarselvam, E., Suresh, P., & Parthasarathy, R. (2013). Segmentation of CT scan lumbar spine image using median filter and canny edge detection algorithm. Int J Comput Sci Eng, 5, 806-814.
13. Punarselvam, E., et al. “Segmentation Analysis Techniques and Identifying Stress Ratio of Human Lumbar Spine Using ANSYS.” Journal of Medical Imaging and Health Informatics 10.10 (2020): 2308-2315.
14. Karthick, R., et al. “Overcome the challenges in bio-medical instruments using IOT–A review.”; Materials Today: Proceedings 45 (2021): 1614-1619.
15. Suresh, Helina Rajini, et al. “Suppression of four wave mixing effect in DWDM system.” Materials Today: Proceedings 45 (2021): 2707-2712.
16. Soundari, D. V., et al.”Enhancing network-on-chip performance by 32-bit RISC processor based on power and area efficiency.” Materials Today: Proceedings 45 (2021): 2713-2720.
17. Sabarish, P., et al., Investigation on performance of solar photovoltaic fed hybrid semi impedance source converters." Materials Today: Proceedings 45 (2021): 1597-1602.
18. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for Ht-MPSOC “; International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41.
19. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020).
20. Sabarish, P., et al., “An Energy Efficient Microwave Based Wireless Solar Power Transmission System” IOP Conference Series: Materials Science and Engineering. Vol. 937.No. 1. IOP Publishing, 2020.
21. Vijayalakshmi, S., et al. ”Implementation of a new Bi-Directional Switch multilevel Inverter for the reduction of harmonics” IOP Conference Series: Materials Science and Engineering. Vol.937. No. 1. IOP Publishing, 2020.
22. Karthick, R., and M. Sundararajan. “Design and implementation of low power testing using advanced razor based processor” International Journal of Applied Engineering Research 12.17 (2017): 6384-6390.
23. Punarselvam, E., and P. Suresh. “Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model.” Journal of medical systems 43, no. 2 (2019): 1-13.
24. Punarselvam, E., Hemalatha, E., Dhivahar, J., Gowtham, V., Hari, V., & Thamaraikannan, R. PREDICTING WIRELESS CHANNELS FOR ULTRA-RELIABLE LOW-LATENCY COMMUNICATIONS.
25. Punarselvam, E., et al. “Segmentation of Lumbar spine image using Watershed Algorithm.” International Journal of Engineering Research and Applications, ISSN (2013): 2248-9622.
26. Punarselvam, E., and P. Suresh. “Edge detection of CT scan spine disc image using canny edge detection algorithm based on magnitude and edge length.” 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011). IEEE, 2011. (2021): 4991-5004.
27. Punarselvam, Dr E., and S. Gopi. “Effective and Efficient Traffic Scrutiny in Sweet Server with Data Privacy.” International Journal on Applications in Information and Communication Engineering 5.2 (2019): 1-5
28. Punarselvam, E., and P. Suresh. “Investigation on human lumbar spine MRI image using finite element method and soft computing techniques.” Cluster Computing 22, no. 6 (2019): 13591-13607.
29. Karthick, R., and M. Sundararajan. “A Reconfigurable Method for Time Correlated Mimo Channels with a Decision Feedback Receiver” International Journal of Applied Engineering Research 12.15 (2017): 5234-5241.
30. Karthick, R., and M. Sundararajan. “A novel 3-D-IC test architecture-a review” International Journal of Engineering and Technology (UAE) 7.1.1 (2018): 582-586.
31. Karthick, R., and M. Sundararajan.,”PSO based out-of-order (ooo) execution scheme for HT-MPSOC” Journal of Advanced Research in Dynamical and Control Systems 9 (2017): 1969.
32. Punarselvam, Dr E., and S. Gopi. “Effective and Efficient Traffic Scrutiny in Sweet Server with Data Privacy.” International Journal on Applications in Information and Communication Engineering 5.2 (2019): 1-5.
33. Punarselvam, E., et al. “Different loading condition and angle measurement of human lumbar spine MRI image using ANSYS.” Journal of Ambient Intelligence and Humanized Computing 12.5
34. Punarselvam, E., and P. Suresh. “Non-Linear Filtering Technique Used for Testing the Human Lumbar Spine FEA Model.” Journal of medical systems 43, no. 2 (2019): 1-13.
35. Punarselvam, E., Hemalatha, E., Dhivahar, J., Gowtham, V., Hari, V., & Thamaraikannan, R. PREDICTING WIRELESS CHANNELS FOR ULTRA-RELIABLE LOW-LATENCY COMMUNICATIONS.
36. Punarselvam, E., et al. “Segmentation of Lumbar spine image using Watershed Algorithm.” International Journal of Engineering Research and Applications, ISSN (2013): 2248-9622.
ABSTRACT:
Chronic Kidney Disease (CKD) is a serious global health issue with high morbidity and mortality rates. Its early stages often present no clear symptoms, causing patients to overlook the disease. Timely detection is essential to improve patient outcomes. An Artificial Intelligence-based computational model for CKD diagnosis can support clinicians by enabling early, accurate predictions. This study proposes a logistic regression system to identify CKD and compares its performance with other machine learning algorithms, including Naive Bayes, Decision Tree, K-Star, and SVM. The dataset used contains missing values, a common issue in real-world medical data, as patients may skip tests. The approach addresses this challenge using imputation and proposes a fused model combining logistic regression and Random Forest with a perceptron-based mechanism. The goal is to reduce misclassification and enhance diagnostic accuracy. This computational approach shows promise in supporting medical professionals through reliable, efficient, and automated CKD detection in clinical settings.
INTRODUCTION:
Chronic Kidney Disease (CKD) remains a prevalent health challenge worldwide. As symptoms are often undetectable in early stages, patients frequently ignore medical attention until the disease progresses. Artificial Intelligence-based computational models for CKD diagnosis have emerged as powerful tools to assist in early detection. However, clinical datasets often contain missing values due to unperformed tests. Traditional imputation techniques like mean substitution may introduce inaccuracies, especially with binary or categorical data, where averaging can distort real values. To address this, the study adopts a feature selection approach to reduce computational complexity and improve predictive performance. Multiple algorithms—Naive Bayes, Decision Tree, K-Star, Logistic Regression, and SVM—were tested, and a hybrid model combining logistic regression with Random Forest using a perceptron layer was developed. This model improves diagnostic reliability by reducing misclassification. By incorporating intelligent data handling and model fusion, the approach strengthens early detection capabilities .
