Development of an ensemble approach to chronic kidney disease diagnosis

Jongbo, Olayinka Ayodele; Adetunmbi, Adebayo Olusola; Ogunrinde, R. B.; Badeji-Ajisafe, Bukola

doi:10.1016/j.sciaf.2020.e00456

Cited by 33 publications

(18 citation statements)

References 4 publications

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“…In our work, the Relief-F feature selection methods have achieved the best performance for the testing results and cross-validation results using DT and GBT Classifier compared to the other existing works [ 23 , 24 , 26 , 27 , 30 ]. Also, our work is different from the other existing works [ 22 , 25 ] because it registered the results for both the training set and the testing set, and it has achieved the best performance.…”

Section: Experiments and Resultsmentioning

confidence: 85%

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Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Abdel-Fattah

Othman

Goher

2022

Computational Intelligence and Neuroscience

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Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatment of people in danger of this disease. The machine learning algorithm is a source of significant assistance for medical scientists to diagnose the disease accurately in its outset stage. Recently, Big Data platforms are integrated with machine learning algorithms to add value to healthcare. Therefore, this paper proposes hybrid machine learning techniques that include feature selection methods and machine learning classification algorithms based on big data platforms (Apache Spark) that were used to detect chronic kidney disease (CKD). The feature selection techniques, namely, Relief-F and chi-squared feature selection method, were applied to select the important features. Six machine learning classification algorithms were used in this research: decision tree (DT), logistic regression (LR), Naive Bayes (NB), Random Forest (RF), support vector machine (SVM), and Gradient-Boosted Trees (GBT Classifier) as ensemble learning algorithms. Four methods of evaluation, namely, accuracy, precision, recall, and F1-measure, were applied to validate the results. For each algorithm, the results of cross-validation and the testing results have been computed based on full features, the features selected by Relief-F, and the features selected by chi-squared feature selection method. The results showed that SVM, DT, and GBT Classifiers with the selected features had achieved the best performance at 100% accuracy. Overall, Relief-F’s selected features are better than full features and the features selected by chi-square.

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Section: Experiments and Resultsmentioning

confidence: 85%

“…The proposed CFS with AdaBoost achieved the best performance at 98.1% accuracy. In [ 25 ], the authors used two ensembles techniques which are Bagging and Random Subspace methods and three base-learners, KNN, NB, and DT, to predict CKD. The random subspace has achieved the best performance than Bagging on KNN classifier.…”

Section: Related Workmentioning

confidence: 99%

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Abdel-Fattah

Othman

Goher

2022

Computational Intelligence and Neuroscience

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“…The result obtained established the accuracy levels of decision tree and ensemble classifier as 95% and 98.8% respectively. This is an improvement on [9] with an accuracy level of 89.2% and 95% for decision tree and ensemble classifier respectively. The result obtained in this research work is also better than that of [4] where the decision tree and ensemble classifier accuracy values obtained are 92% and 94.25% respectively.…”

Section: Resultsmentioning

confidence: 94%

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Olasunkanmi

Olanloye

Adegbiji

2020

International Journal of Applied Mathematics Electronics and Computers

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The world is now in the era of big data and processing, and exploring the data has become one of the significant challenges. Hence, researchers have done a lot to analyse these data in the health sector to enhance disease detection and classification using artificial intelligence and ML principles. Kidney disease is one of the terrible conditions in which its late detection has sent many people to untimely graves. ML classifiers have been employed in many dimensions to classify heart disease, but, existing works have not explored the variants of each method for selection of best model parameters. An attempt is being made in this research to study the behaviour of three (3) variants each from two(2) tree-based models in the classification of Kidney Disease. Three of the variants are Complex, Medium and Simple models of Decision tree classifier and the other one are Boosted, Bagged and RUSBoosted of Ensemble Classifiers. Using MATLAB for implementation, the model performance established that the accuracy of Ensemble Classifier (Bagged tree model) is the best, concerning the speed, Decision tree (Complex and Simple tree models have the same and highest value). Hence, the two are the best. In terms of training time, Decision tree(Simple tree) has the least time and therefore the best.

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“…Set of two ensemble models such as Bagging and Random Subspace methods on three base-learners k Nearest Neighbours, Naïve Bayes and Decision Tree has been presented in [24] to improve the classifier outcome. The presented model involves data preprocessing for handling the missing values and data scaling for the normalization of the range of independent variables.…”

Section: Related Workmentioning

confidence: 99%

“…The FS issue for CKD is assumed to be a combinatorial optimization problem. The count of viable feature integration from a feature set which has 24 features that are computed as 2 24 -1. It refers that, it is not possible to process the estimation of every feature combinations.…”

Section: Optimal Feature Subset Selection Problemmentioning

confidence: 99%

Intelligent Chronic Kidney Disease Diagnosis System using Cloud Centric Optimal Feature Subset Selection with Novel Data Classification Model

Arulanthu

Perumal

2021

Preprint

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Internet of Things (IoT) and cloud computing offers diverse applications in the medicinal sector by the integration of sensing and therapeutic gadgets. Medical expenses are rising gradually and different new diseases also exist globally, it becomes essential to transform the healthcare facilities from a hospital to patient-centric platform. For providing effective remote healthcare services to patients, this paper introduces an optimal IoT and cloud based decision support system for Chronic Kidney Disease (CKD) diagnosis. The proposed method makes use of simulated annealing (SA) based feature selection (FS) with Root Mean Square Propagation (RMSProp) Optimizer based Logistic Regression (LR) model called SA-RMSPO-LR to classify the existence of CKD from medical data. The proposed model involves a set of four subprocesses, which include data collection, preprocessing, FS, and classification. The inclusion of SA for FS helps to improvise the classifier results of the SA-RMSPO-LR model. The effectiveness of the SA-RMSPO-LR model has been validated using a benchmark CKD dataset. The experimental results indicated that the proposed SA-RMSPO-LR model leads to effective CKD classification with the maximum sensitivity of 98.41%, specificity of 97.99%, accuracy of 98.25%, F-score of 98.60% and kappa value of 96.26%. The experimental outcome indicates that the proposed SA-RMSPO-LR model has the capability to detect and classify CKD over the compared methods proficiently.

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Development of an ensemble approach to chronic kidney disease diagnosis

Cited by 33 publications

References 4 publications

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Comparison analysis of decision tree and ensemble models in the classification of chronic kidney diseases

Intelligent Chronic Kidney Disease Diagnosis System using Cloud Centric Optimal Feature Subset Selection with Novel Data Classification Model

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