DOI: 10.57760/sciencedb.19287
title: A COMPARATIVE STUDY OF MACHINE LEARNING APPROACHES FOR CHRONIC KIDNEY DISEASE PREDICTION
title: A COMPARATIVE STUDY OF MACHINE LEARNING APPROACHES FOR CHRONIC KIDNEY DISEASE PREDICTION

description: This paper, titled "A Comparative Study of Machine Learning Approaches for Chronic Kidney Disease Prediction," aligns with the scope of Pattern Recognition and Artificial Intelligence (PR&AI) by leveraging advanced machine learning techniques to address a critical healthcare problem. Chronic Kidney Disease (CKD) poses a significant global health challenge, and early diagnosis is vital to mitigate complications and improve patient outcomes.The study explores six machine learning algorithms—Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), CatBoost, and LightGBM—using a dataset with 25 health-related features. By evaluating models through metrics like Accuracy, ROC AUC, Precision, Recall, F1-Score, and Average Precision, the research demonstrates the superiority of ensemble methods such as GBDT, CatBoost, and LightGBM, which achieved over 98% accuracy and ROC AUC. The findings emphasize the effectiveness of predictive modeling for CKD diagnosis, providing valuable insights into algorithmic trade-offs and real-world applicability.This work is relevant to PR&AI’s focus on artificial intelligence, pattern recognition, and intelligent systems, contributing to the journal’s mission of advancing information science and technology. It also highlights the integration of machine learning into intelligent healthcare systems, making it an ideal candidate for the journal's Research and Applications or Papers and Reports sections.
description: This paper, titled "A Comparative Study of Machine Learning Approaches for Chronic Kidney Disease Prediction," aligns with the scope of Pattern Recognition and Artificial Intelligence (PR&AI) by leveraging advanced machine learning techniques to address a critical healthcare problem. Chronic Kidney Disease (CKD) poses a significant global health challenge, and early diagnosis is vital to mitigate complications and improve patient outcomes.The study explores six machine learning algorithms—Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), CatBoost, and LightGBM—using a dataset with 25 health-related features. By evaluating models through metrics like Accuracy, ROC AUC, Precision, Recall, F1-Score, and Average Precision, the research demonstrates the superiority of ensemble methods such as GBDT, CatBoost, and LightGBM, which achieved over 98% accuracy and ROC AUC. The findings emphasize the effectiveness of predictive modeling for CKD diagnosis, providing valuable insights into algorithmic trade-offs and real-world applicability.This work is relevant to PR&AI’s focus on artificial intelligence, pattern recognition, and intelligent systems, contributing to the journal’s mission of advancing information science and technology. It also highlights the integration of machine learning into intelligent healthcare systems, making it an ideal candidate for the journal's Research and Applications or Papers and Reports sections.