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Early identification is necessary for the deadly illness known as chronic kidney disease. By correctly detecting illnesses, machine-learning algorithms like Decision Tree, Support Vector Machine, Xg Boost, and K-Nearest Neighbors have enhanced medical treatment. Our accuracy rates for chronic renal illness using 25 features from the UCI Machine Learning library dataset were 97.23%, 95.70%, 98%, and 62%. With a 98% accuracy rate, Xg Boost produced the best outcomes. Nonlinear traits and categories were used in the development of the Kidney Disease Collection. Overall, our study created and verified a method for applying machine-learning algorithms to predict chronic renal illness.

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Enhancing Kidney Disease Prediction Using the XGBoost Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • A. Vijayaraj,
  • V. P. Murugan,
  • V. R. Thejeshwar,
  • M. Shalini,
  • N. Priyadharshini,
  • S. Sindhuja,
  • C. Vasundhara,
  • D. Amirdavarthini

摘要

Early identification is necessary for the deadly illness known as chronic kidney disease. By correctly detecting illnesses, machine-learning algorithms like Decision Tree, Support Vector Machine, Xg Boost, and K-Nearest Neighbors have enhanced medical treatment. Our accuracy rates for chronic renal illness using 25 features from the UCI Machine Learning library dataset were 97.23%, 95.70%, 98%, and 62%. With a 98% accuracy rate, Xg Boost produced the best outcomes. Nonlinear traits and categories were used in the development of the Kidney Disease Collection. Overall, our study created and verified a method for applying machine-learning algorithms to predict chronic renal illness.