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 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.

Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms

“… Han et al [ 9 ] West China Hospital LR RF DT SVM ANN KNN 93.74% 93.97% 91.21% 87.79% 91.28% 92.80% The authors did not use any feature selection techniques to improve accuracy. Ilyas et al [ 10 ] UCI J48 RF 85.50% 78.25% Authors performed prediction based on the value of GFR, CKD may be categorized only into six stages. They did not use any algorithms for selecting important attributes.…”

Detection of Kidney Disease Using Machine Learning

“…According to a study byHamida Ilyas, Sajid Ali, Mahvish Ponum, corresponding author, Osman Hasan, Muhammad Tahir Mahmood, Mehwish Iftikhar, and Mubasher Hussain Malik [1],chronic kidney disease (CKD) using decision tree algorithms. The authors used a dataset of 400 patients with CKD and 400 healthy individuals to train and test their models.…”

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

“…At the end of the algorithm, all attributes are evaluated and therefore all samples have a target value accordingly [21]. J48 became a popular machine learning tool in many areas due to its easy implementable and robust nature [20,22,23].…”