Abstract
Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD are required. The novelty of this study lies in developing the diagnosis system to detect chronic kidney diseases. This study assists experts in exploring preventive measures for CKD through early diagnosis using machine learning techniques. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and the nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Four classification algorithms applied in this study were support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and random forest. All the classification algorithms achieved promising performance. The random forest algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100% for all measures. CKD is a serious life-threatening disease, with high rates of morbidity and mortality. Therefore, artificial intelligence techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.
Highlights
Chronic kidney disease (CKD) has received much attention due to its high mortality rate
It is called “chronic” disease because the kidney disease begins gradually and lasts for a Journal of Healthcare Engineering long time, which affects the functioning of the urinary system. e accumulation of waste products in the blood leads to the emergence of other health problems, which are associated with several symptoms such as high and low blood pressure, diabetes, nerve damage, and bone problems, which lead to cardiovascular disease
Wibawa et al [15] applied correlation-based feature selection (CFS) for feature selection, and AdaBoost for ensemble learning was applied to improve CKD diagnosis. e k-nearest neighbors (KNN), naive Bayes, and support vector machine (SVM) algorithms were applied for CKD dataset diagnosis. eir system achieved the best accuracy when implementing a hybrid between KNN with CFS and AdaBoost by 98.1%
Summary
Chronic kidney disease (CKD) has received much attention due to its high mortality rate. Almansour et al [12] diagnosed a CKD dataset using ANN and SVM algorithms. Rady and Anwar [13] applied probabilistic neural networks (PNN), multilayer perceptron (MLP), SVM, and radial basis function (RBF) algorithms to diagnose CKD dataset. Kunwar et al [14] applied two algorithms—naive Bayes and artificial neural networks (ANN)— to diagnose a UCI dataset for CKD. E KNN, naive Bayes, and SVM algorithms were applied for CKD dataset diagnosis. Kunwar et al [14] used Artificial Neural Network (ANN) and Naive Bayes to evaluate a UCI dataset of 400 patients. En, a new model with linear regression (LR) and neural network (NN) was applied to evaluate the performance of their VMs for diagnosing CKD. Ravizza et al [22] applied a model to test
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