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The diagnosis of kidney disease, often called chronic renal illness, is known as chronic kidney disease (CKD). Chronic kidney disease (CKD) is a widespread and chronic health issue that requires preventative measures for early identification in order to slow the disease's progression. Using complex techniques like Support Vector Machine (SVM) and Logistic Regression, this study explores the field of machine learning. For robust model construction, use Random Forest and Decision Tree. Through the use of Variance Inflation Factor (VIF) for feature engineering, the dataset is carefully refined to improve the visibility of relevant features. To address any possible data imbalance concerns, Synthetic Minority Over-sampling Technique (SMOTE) is also utilized, promoting a fairer representation of classes in the dataset. By means of a thorough assessment procedure, the study methodically pinpoints the key elements that contribute to a precise diagnosis of chronic kidney disease. Each algorithm's effectiveness is evaluated using performance indicators like recall, accuracy, precision, and Fl-score.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Dr. R. Triveni, triveni.aishu@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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