Boosted classifier and features selection for enhancing chronic kidney disease diagnose

Wibawa, Made Satria; Maysanjaya, I Made Dendi; Putra, I Made Agus Wirahadi

doi:10.1109/citsm.2017.8089245

Cited by 53 publications

(32 citation statements)

References 13 publications

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“…This is attributable to the fact that the reported studies together covered the performance analysis of most known machine learning techniques, as addressed in the next paragraphs. Most of the studies used the same 24 attributes (or their subset) from the University of California Irvine (UCI) machine learning repository, i.e., 18 from the 27 accepted papers [23], [26]- [42]. However, these existing studies have some limitations, which indicate at least one research gap: lack of a comparative analysis of machine learning techniques considering specific characteristics of developing countries, such as high levels of poverty and hard-to-reach settings.…”

Section: ) Protocolmentioning

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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Section: ) Protocolmentioning

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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“…Wibawa, M. S., et al have introduced an approach to improve the superiority of CKD [20]. This model contributes in 3 steps like FS, ensemble learning as well as classification process.…”

Section: Related Workmentioning

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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“…Salekin and Stankovic [11] investigated the CKD diagnosis problem from the cost-accuracy trade-off perspective. The authors demonstrated the efficacy of machine learning-based CKD diagnosis in terms of identifying useful features that are not taken into account by GFR estimation equations.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Feature Ranking for Cost-Based Non-Overlapping Groups: A Case Study of Chronic Kidney Disease Diagnosis in Developing Countries

et al. 2020

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Chronic kidney disease (CKD) is one of the leading medical ailments in developing countries. Due to the limited healthcare infrastructure and the lack of trained human resources, the CKD problem aggravates if it is not addressed in its earlier stages. In this regard, the role of machine learning-based automated diagnosis systems plays a vital role to deal with the CKD problem. In most of the studies conducted on the automated CKD decision modeling, the main emphasis is given to enhancing the predictive accuracy of the system. In this study, we focus on the applicability challenges of automated decision systems taking CKD diagnosis as a case study within the purview of developing countries. In this regard, we propose a cost-sensitive ensemble feature ranking method that takes a more realistic approach to group-based feature selection. Two candidate solutions are proposed for group-based feature selection to meet different objectives. Subsequently, both the candidate solutions are combined into a consolidated solution. It is pertinent to note that it is one of the first studies in which cost-sensitive ensemble feature ranking for non-overlapping groups is successfully demonstrated to achieve the stated objectives i.e. low-cost and high-accuracy solution. Based on an extensive set of experiments, we demonstrate that a cost-effective and accurate solution for the CKD problem can be obtained. The experimentation includes 7 well-known classification algorithms and 8 comparative feature selection methods to show the efficacy of the proposed approach. It is concluded that the applicability of the automated CKD systems can be enhanced by including the cost consideration into the objective space of the solution formulation. Therefore, a trade-off solution can be obtained that is cost-effective and yet accurate enough to serve as a CKD screening system. INDEX TERMS Ensemble feature ranking, cost-based feature selection, threshold selection, filter methods

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Boosted classifier and features selection for enhancing chronic kidney disease diagnose

Cited by 53 publications

References 13 publications

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

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

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

Ensemble Feature Ranking for Cost-Based Non-Overlapping Groups: A Case Study of Chronic Kidney Disease Diagnosis in Developing Countries

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