2023
doi: 10.12720/jait.14.2.384-391
Chronic Kidney Disease Prediction Using Machine Learning
Chamandeep Kaur1
,
M. Sunil Kumar2 ,
Afsana Anjum1 , ,
Maheswara Reddy Mallu4 ,
Mohammed Saleh Al Ansari5,*
- 1Department of Computer Science & Information Technology, Jazan University, Jizan, Saudi Arabia
- 2School of Computing, Department of CSE, Mohan Babu University & Sree Vidyanikethan Engineering College Tirupati, AP, India
- 3Traffic Signal Division, Keltron Communication Complex, Thiruvananthapuram, Kerala, India
- 4Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522302, Guntur, Andhra Pradesh, India
- 5Department of Chemical Engineering, University of Bahrain, Bahrain
- Manuscript receivedSeptember 19, 2022
- revisedOctober 12, 2022
- acceptedDecember 21, 2022
- publishedApril 26, 2023
Abstract
The occurrence of Chronic Renal Disease (CRD), is also referred to as Chronic Kidney Disease (CKD). It depicts a medical condition that harms the kidneys and has an impact on a person’s overall health. End-stage renal disease and the patient’s eventual mortality can result from improper disease diagnosis and treatment. In the field of medical science, Machine Learning (ML) techniques have become a valuable tool and play a significant role in disease prediction. The development and validation of a predictive model for the prognosis of chronic renal disease is the aim of the proposed study. A dataset on chronic kidney disease with 400 samples was taken from the UCI Machine Learning Repository. Three machine learning classifiers—Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM)—were used for analysis, and the bagging ensemble method was used to enhance the model’s performance. The machine learning classifiers were trained using the clusters of the dataset for chronic renal disease. The Kidney Disease Collection is then compiled using non-linear features and categories. The decision tree produces the best results, with an accuracy of 95%. Finally, we achieve the greatest accuracy of 97% by using the bagging ensemble approach.
Keywords
- chronic renal disease
- classification algorithms
- random forest classifier
- machine learning
How to Cite
Chamandeep Kaur, M. Sunil Kumar, Afsana Anjum, M. B. Binda, Maheswara Reddy Mallu, and Mohammed Saleh Al Ansari, "Chronic Kidney Disease Prediction Using Machine Learning," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 384-391, 2023. https://doi.org/10.12720/jait.14.2.384-391
Copyright & License
Copyright © 2023 by the authors. 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 (CC BY 4.0).
