Chronic Kidney Disease Detection Using Machine Learning Model

Abstract

Chronic Kidney Disease (CKD) is a long-term medical condition in which the kidneys gradually lose their ability to function properly. Also known as chronic renal failure, this disease progresses over a period of several months or even years, often without obvious symptoms in its early stages. Because of this, CKD is frequently diagnosed only through routine medical examinations, especially in individuals with existing risk factors like diabetes, high blood pressure, or a family history of kidney issues. Detecting CKD early is essential for managing the illness and preventing serious complications. With early diagnosis being difficult due to the lack of visible signs, healthcare professionals have increasingly turned to Machine Learning (ML) as a modern solution. ML techniques can analyze clinical datasets to detect patterns and predict disease risk with impressive accuracy. Among these techniques, the Random Forest algorithm has proven particularly effective in identifying CKD, outperforming many other models in terms of prediction reliability.

Introduction

Chronic Kidney Disease (CKD) is a progressive condition in which the kidneys gradually lose their ability to filter waste, regulate fluids and electrolytes, and support key body functions such as blood pressure control and red blood cell production. Common causes include diabetes, hypertension, infections, autoimmune disorders, and long-term use of harmful medications. Early CKD is often symptomless, making timely detection difficult without regular testing. As the disease advances, symptoms such as fatigue, swelling, nausea, and urination changes may develop, and untreated CKD can progress to end-stage renal disease (ESRD), requiring dialysis or a kidney transplant. CKD is classified into five stages based on the glomerular filtration rate (GFR).

Research in this field highlights major risk factors, biochemical markers for diagnosis, and the growing use of machine learning (ML) to enhance early detection and patient management. Various ML models—such as decision trees, SVMs, and neural networks—have shown strong predictive capability for CKD.

The methodology described involves building an ML-based CKD prediction system using a dataset from the UCI Machine Learning Repository. The data is cleaned, preprocessed, and used to train a Random Forest model chosen for its accuracy and robustness. The model’s performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and a confusion matrix. Once validated, it is integrated into a Flask-based web application that enables users to input clinical data and receive real-time CKD predictions. The system architecture includes modules for data preprocessing, model training, evaluation, model saving, and user interface development. All components are combined to create an accessible and efficient CKD prediction tool.

Conclusion

In this project, a machine learning-based system was developed to predict Chronic Kidney Disease (CKD) using the Random Forest algorithm. The model was trained on a publicly available dataset and successfully classified individuals as either healthy or affected by CKD, along with indicating the severity level (high or low). The Random Forest classifier demonstrated strong performance in terms of accuracy and reliability, making it a suitable choice for medical prediction tasks. The system’s ability to deliver clear, real-time results through a user-friendly web interface enhances its usability in practical healthcare settings. By enabling early detection and classification of CKD, this model can assist medical professionals in making informed clinical decisions and improve patient outcomes. The future scope for a chronic kidney disease (CKD) project using a random forest algorithm could involve refining the model with more data, enhancing feature selection techniques, exploring ensemble methods, and integrating it into healthcare systems for early detection and personalized treatment recommendations. Another potential avenue could be the development of a user-friendly mobile app that integrates the CKD prediction model, allowing users to assess their risk of CKD and receive personalized recommendations for lifestyle modifications.

References

[1] M. P. N. M. Wickramasinghe, D. M. Perera, and K. A. D. C. P. Kahandawaarachchi, “Dietary prediction for patients with chronic kidney disease (CKD) by considering blood potassium level using machine learning algorithms,” 2017 IEEE Life Sciences Conference (LSC), Sydney, NSW, 2017, pp. 300–303. [2] U. N. Dulhare and M. Ayesha, “Extraction of action rules for chronic kidney disease using Naïve Bayes classifier,” 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016, pp. 1–5. [3] R. Devika, S. V. Avilala, and V. Subramaniyaswamy, “Comparative Study of Classifier for Chronic Kidney Disease Prediction Using Naïve Bayes, KNN and Random Forest2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, pp. 679–684. [4] G. Kaur and A. Sharma,“Predict chronic kidney disease using data mining algorithms in Hadoop,” 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 973–979. [5] Arif-Ul-Islam and S. H. Ripon, “Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Casazza, Bangladesh, 2019, pp. 1–6 [6] 6. S. Vijayarani and S. Dayanand, “Kidney Disease Prediction Using SVM and ANN Algorithms,” International Journal of Computing and Business Research (IJCBR), vol. 6, no. 2, 2015. [7] D. Sisodia and D. S. Sisodia, “Prediction of Diabetes Using Three Classification Algorithms,” International Conference on Computational Intelligence and Data Science (ICCIDS), 2018. [8] M. S. Gharibdousti, K. Azimi, S. H. Kal, and D. H. Won, “Prediction of Chronic Kidney Disease Using Data Mining Techniques,” 2018.

Copyright

Copyright © 2025 Vadthyavath Shirisha, Dr. M. Dhanalakshmi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.