Scholarlink

In the modern world, chronic kidney disease (CKD) can have a devastating impact on human survival. Accurate and timely detection of CKD is of great importance for the prevention and treatment of renal failure. Non-invasive techniques such as Machine Learning models (ML) provide a high degree of reliability and efficiency in distinguishing healthy individuals from those suffering from CKD. The goal of this research is to develop a unified method for predicting CKD by applying the classification algorithms of ML to the UCI repository dataset and the medical records of affected individuals. To build the prediction system, the authors used all the basic preprocessing methods from ML and a hybrid extraction technique to reduce dimensionality. Analysis of the results with two different datasets showed that the highest accuracy of 95.83% was achieved by the Nu- SVC classifier with medical records of kidney patients. an accuracy of 99% was achieved with the standard CKD dataset.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Validation of a Chronic Kidney Disease Prediction System Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Vineeta Gulati,
  • Neeraj Raheja,
  • Arun Kumar Rana,
  • Sandeep Kajal

摘要

In the modern world, chronic kidney disease (CKD) can have a devastating impact on human survival. Accurate and timely detection of CKD is of great importance for the prevention and treatment of renal failure. Non-invasive techniques such as Machine Learning models (ML) provide a high degree of reliability and efficiency in distinguishing healthy individuals from those suffering from CKD. The goal of this research is to develop a unified method for predicting CKD by applying the classification algorithms of ML to the UCI repository dataset and the medical records of affected individuals. To build the prediction system, the authors used all the basic preprocessing methods from ML and a hybrid extraction technique to reduce dimensionality. Analysis of the results with two different datasets showed that the highest accuracy of 95.83% was achieved by the Nu- SVC classifier with medical records of kidney patients. an accuracy of 99% was achieved with the standard CKD dataset.