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Chronic Renal (Kidney) Disease Prediction using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine learning
Abstract
Chronic Kidney Disease (CKD) is a progressive medical condition that, if left undiagnosed or untreated, can lead to kidney failure and severe health complications. Early detection is crucial to managing the disease and improving patient outcomes. This project aims to develop an intelligent system that accurately predicts the likelihood of CKD based on various clinical parameters such as age, blood pressure, serum creatinine, and others. The system employs advanced data preprocessing techniques, exploratory data analysis, and feature selection methods including correlation heatmaps, LASSO regularization, and wrapper-based techniques to identify the most significant features.Multiple machine learning algorithms?including Random Forest, Support Vector Machine (SVM), Decision Tree, Logistic Regression, XGBoost, and deep learning hybrid models?are trained and evaluated using performance metrics like accuracy, precision, recall, F1-score, and AUC-ROC. The best-performing model is integrated into a user-friendly web application built using Flask, with a front end developed using HTML, CSS, and JavaScript. The application allows users to input medical parameters and instantly receives a CKD risk prediction. This system not only aids healthcare professionals in early diagnosis but also empowers users with a tool for proactive health monitoring. The solution is scalable, interpretable, and can be continuously updated with new data to improve prediction accuracy and reliability.
Keywords
Chronic Kidney Disease (CKD),Machine Learning (ML),Healthcare AnalyticsData Mining, Early Detection Classification Algorithms,Clinical Decision Support, Predictive Modeling,webapp.
How to cite this paper
@article{1710367,
author = {Gangotri Subhash Basagoudar, Kivudi Chikkapla Priyanaka, Devika M, Sandesh Mallappa Kalagi, Bhagyashri Wakde},
title = {Chronic Renal (Kidney) Disease Prediction using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {53-57},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710367.pdf},
abstract = {Chronic Kidney Disease (CKD) is a progressive medical condition that, if left undiagnosed or untreated, can lead to kidney failure and severe health complications. Early detection is crucial to managing the disease and improving patient outcomes. This project aims to develop an intelligent system that accurately predicts the likelihood of CKD based on various clinical parameters such as age, blood pressure, serum creatinine, and others. The system employs advanced data preprocessing techniques, exploratory data analysis, and feature selection methods including correlation heatmaps, LASSO regularization, and wrapper-based techniques to identify the most significant features.Multiple machine learning algorithms?including Random Forest, Support Vector Machine (SVM), Decision Tree, Logistic Regression, XGBoost, and deep learning hybrid models?are trained and evaluated using performance metrics like accuracy, precision, recall, F1-score, and AUC-ROC. The best-performing model is integrated into a user-friendly web application built using Flask, with a front end developed using HTML, CSS, and JavaScript. The application allows users to input medical parameters and instantly receives a CKD risk prediction. This system not only aids healthcare professionals in early diagnosis but also empowers users with a tool for proactive health monitoring. The solution is scalable, interpretable, and can be continuously updated with new data to improve prediction accuracy and reliability.},
keywords = {Chronic Kidney Disease (CKD),Machine Learning (ML),Healthcare AnalyticsData Mining, Early Detection Classification Algorithms,Clinical Decision Support, Predictive Modeling,webapp.},
month = {September},
}