Chronic kidney disease diagnosis using decision tree algorithms
Abstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various st...
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-021-02474-z |
| _version_ | 1818444008092860416 |
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| author | Hamida Ilyas Sajid Ali Mahvish Ponum Osman Hasan Muhammad Tahir Mahmood Mehwish Iftikhar Mubasher Hussain Malik |
| author_facet | Hamida Ilyas Sajid Ali Mahvish Ponum Osman Hasan Muhammad Tahir Mahmood Mehwish Iftikhar Mubasher Hussain Malik |
| author_sort | Hamida Ilyas |
| collection | DOAJ |
| description | Abstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD. |
| first_indexed | 2024-12-14T19:09:06Z |
| format | Article |
| id | doaj.art-728e8c0583e14bc99ce2887df011c5e9 |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-12-14T19:09:06Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-728e8c0583e14bc99ce2887df011c5e92022-12-21T22:50:46ZengBMCBMC Nephrology1471-23692021-08-0122111110.1186/s12882-021-02474-zChronic kidney disease diagnosis using decision tree algorithmsHamida Ilyas0Sajid Ali1Mahvish Ponum2Osman Hasan3Muhammad Tahir Mahmood4Mehwish Iftikhar5Mubasher Hussain Malik6School of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologySchool of Electrical Engineering and Computer Science, National University of Sciences and TechnologyAbstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.https://doi.org/10.1186/s12882-021-02474-zCKDGFRMachine learningDecision treeJ48Random Forest |
| spellingShingle | Hamida Ilyas Sajid Ali Mahvish Ponum Osman Hasan Muhammad Tahir Mahmood Mehwish Iftikhar Mubasher Hussain Malik Chronic kidney disease diagnosis using decision tree algorithms BMC Nephrology CKD GFR Machine learning Decision tree J48 Random Forest |
| title | Chronic kidney disease diagnosis using decision tree algorithms |
| title_full | Chronic kidney disease diagnosis using decision tree algorithms |
| title_fullStr | Chronic kidney disease diagnosis using decision tree algorithms |
| title_full_unstemmed | Chronic kidney disease diagnosis using decision tree algorithms |
| title_short | Chronic kidney disease diagnosis using decision tree algorithms |
| title_sort | chronic kidney disease diagnosis using decision tree algorithms |
| topic | CKD GFR Machine learning Decision tree J48 Random Forest |
| url | https://doi.org/10.1186/s12882-021-02474-z |
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