A Review on Machine Learning Approaches for HIV Infected Patient Chronic Kidney Disease Stage Classification

Authors

  • Manisha Makwana  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dr. Rocky Upadhyay  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dr. Sheshang Degadwala  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dhairya Vyas  Managing Director, Shree Drashti Infotech LLP, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT228662

Keywords:

Chronic Kidney Disease, Support Vector Machine, K-nearest Neighbor, Random Forest, Decision Tree, Navier Bayes.

Abstract

Chronic kidney disease (CKD) is sometimes called chronic kidney failure. The kidneys eliminate waste and surplus fluids from the circulation and excrete them as urine. In severe chronic renal disease, fluid, electrolytes, and waste products may build up in the body. HIV patients with additional risk factors for renal disease must have kidney function evaluated annually. HIV may damage kidney filters. The filters won't work properly. CKD has five stages, with more severe symptoms from stage 1 to stage 5. If chronic kidney disease continues to stage 4 or 5, our bodies might accumulate fluid and waste. Machine learning categories HIV-positive CKD patients based on their features. Machine learning relies on feature selection. This research uses feature selection and classification to accurately predict chronic renal illness.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-12-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

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How to Cite

[1]
Manisha Makwana, Dr. Rocky Upadhyay, Dr. Sheshang Degadwala, Dhairya Vyas, " A Review on Machine Learning Approaches for HIV Infected Patient Chronic Kidney Disease Stage Classification, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.400-408, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT228662