Detection of chronic kidney disease by using Adaboost ensemble learning approach

Başar, Merve Doğruyol; Sari, Pelin; Kılıç, Niyazi; Akan, Aydın

doi:10.1109/siu.2016.7495854

Cited by 14 publications

(12 citation statements)

References 10 publications

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“…Literatures reviewed indicated that ensemble classifiers are better than individual classifiers- [8], [16]. Apart from supporting these results, the proposed model shows some improvement compared to the existing models.…”

Section: Resultssupporting

confidence: 58%

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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Section: Resultssupporting

confidence: 58%

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 NB, RF, bagging/J48 decision tree, and the random subspace/J48 decision tree methods presented the highest accuracy (100%) when analyzing 24 CKD-related attributes from the UCI Machine Learning Repository [36]- [38]. Considering the same 24 attributes, the multilayer perceptron (MLP) technique had an accuracy of 99.75% [39], while the SVM and AdaBoost techniques had an accuracy of 99% [40], [41].…”

Section: C: Addressing Rq 13mentioning

confidence: 99%

Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

et al. 2020

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The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist's opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results. INDEX TERMS Reviews, machine learning, medical diagnosis.

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“…We showed that both ensemble algorithms and proposed feature selection methods are efficient tools to classify CKD. However, our performance measurement metrics changed differently [18,19].…”

Section: Resultsmentioning

confidence: 99%

Chronic Kidney Disease Prediction with Reduced Individual Classifiers

Başar¹,

Akan²

2018

Electrica

Self Cite

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Chronic kidney disease is a rising health problem and involves conditions that decrease the efficiency of renal functions and that damage the kidneys. Chronic kidney disease may be detected with several classification techniques, and these have been classified using various features and classifier combinations. In this study, we applied seven different classifiers (Naïve Bayes, HoeffdingTree, RandomTree, REPTree, Random Subspaces, Adaboost, and IBk) for the diagnosis of chronic kidney disease. The classification performances are evaluated with five different performance metrics, i.e., accuracy, kappa, mean absolute error (MAE), root mean square error (RMSE), and F measures. Considering the classification performance analyses of these methods, six reduced features provide a better and more rapid classification performance. Seven individual classifiers are applied to the six features and the best results are obtained using individual random tree and IBk classifiers.

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Detection of chronic kidney disease by using Adaboost ensemble learning approach

Cited by 14 publications

References 10 publications

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

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

Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

Chronic Kidney Disease Prediction with Reduced Individual Classifiers

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