Paper Key : IRJ************859
Author: Saini Kavya
Date Published: 02 Feb 2025
Abstract
Chronic Kidney Disease (CKD) is one of the biggest global health issues. Early detection of CKD can prevent major complications. The current project involves the application of machine learning algorithms for predicting CKD using clinical data. Performance of several algorithms such as Support Vector Machine, Kernel SVM, Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, Naive Bayes, and Decision Tree have been compared. Each model was assessed on a preprocessed dataset based on accuracy as the main performance metric. This paper aims to identify the most effective algorithm to predict CKD and emphasizes the role of machine learning in health care.Our findings show that the best-performing algorithm was insert best-performing algorithm, which achieved the highest accuracy. Thus, this algorithm is the most reliable for the prediction of CKD. This study also investigates the strengths and limitations of each algorithm, allowing the reader to gain insight into their performance in healthcare applications. Such findings reveal the potential for machine learning in improving early diagnosis and patient outcomes, further advancing data-driven solutions for healthcare.
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