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Chronic kidney disease diagnosis using decision tree algorithms
Hamida Ilyas1,2, Sajid Ali1,2,3, Mahvish Ponum4
1School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H/12 Sector, Islamabad, Pakistan.
Insights
This study used machine learning to predict Chronic Kidney Disease (CKD) stages. The J48 algorithm achieved 85.5% accuracy, outperforming Random Forest for early CKD detection.
Area of Science:
- Nephrology
- Medical Informatics
- Machine Learning
Background:
- Chronic Kidney Disease (CKD) is a progressive, asymptomatic decline in renal function over months to years.
- CKD is staged based on Glomerular Filtration Rate (GFR), influenced by factors like age, sex, race, and serum creatinine.
- The CKD-EPI linear model is efficient for GFR estimation and CKD stage detection.
Purpose of the Study:
- To predict various stages of Chronic Kidney Disease (CKD) using machine learning classification algorithms.
- To develop a sustainable and practicable model for detecting CKD stages with high medical accuracy.
- To leverage machine learning for early symptom detection and diagnosis of CKD.
Main Methods:
- Utilized machine learning classification algorithms, specifically Random Forest and J48.
- Applied algorithms to a dataset derived from medical records of individuals with CKD.
- Compared the performance of J48 and Random Forest algorithms for CKD stage prediction.
Main Results:
- The J48 algorithm demonstrated superior performance in predicting all stages of CKD compared to Random Forest.
- J48 achieved an accuracy of 85.5% in detecting CKD stages.
- Comparative analysis confirmed J48's improved performance over the Random Forest algorithm.
Conclusions:
- The J48 algorithm shows significant potential for accurate CKD stage detection.
- An automated system for detecting CKD severity can be developed using these machine learning models.
- Early and accurate detection of CKD stages is crucial for preventing adverse health outcomes.
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.
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Related Concept Videos
01:25Chronic Kidney Disease I: Introduction
01:28Chronic Kidney Disease III: Interprofessional Care
01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention
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01:24Chronic Kidney Disease II: Clinical Manifestations