Robust Chronic Kidney Impact Identification System Using Prab Algorithm

Authors

  • S. Mohammed Imran Department of Computer Applications, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  • N. Prakash Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  • K. Hazeena Department of Computer Applications, MEASI Institute of Information Technology, Chennai, India
  • S. Sivasubramanian Department of Information Technology, New Prince Shri Bhavani College of Engineering and Technology, Anna University, Chennai, India

Keywords:

Chronic kidney disease, adaptive boosting, data analytics, machine learning, exploratory data analysis

Abstract

Kidneys act as one of the important organs of the body.  Kidney disease needs to be treated in the early stages to protect Human life. Chronic kidney disease slowly impacts the functionality of kidneys that make severe damage to kidneys if untreated. Chronic kidney disease affects other organs of the body and creates life threatening problems.  Early prediction of kidney disease can save the life of the person and reduce the financial cost taken for treatment purposes. The proposed approach is focused on developing a robust algorithm named probability weighted AdaBoost to predict chronic kidney disease. The adaptive weight calculation of Adaboost algorithm (PRAB) is modified here to provide the reduction of incorrectly classified values on kidney disease detection. The existence of false positive data is one of the major drawbacks produces that induces similarity issues in classification. To avoid the weak classification criteria PRAB algorithm is utilized here. The adaptive selections of classifier with removal of weak data analyzer enhance the accuracy of prediction.

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Published

05.12.2023

How to Cite

Imran, S. M. ., Prakash, N. ., Hazeena, K. ., & Sivasubramanian, S. . (2023). Robust Chronic Kidney Impact Identification System Using Prab Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 404–411. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4089

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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