Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique

Tazin, Nusrat; Sabab, Shahed Anzarus; Chowdhury, Muhammed Tawfiq

doi:10.1109/meditec.2016.7835365

Cited by 42 publications

(16 citation statements)

References 7 publications

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“…In an another study the same framework is used as reported in [20], in which they used three classifiers on a CKD dataset complied from Prince Hamza Hospital, Jordan. The study reported that the decision tree model performed reasonably well on a number of performance metrics [21]. N. Tazin et al [21] used a number of classification models such as SVM, Naive Bayes, KNN and decision tree on CKD dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Ali¹,

Park²,

Lee³

2020

Preprint

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Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general purpose classification problems, medical applications require special treatment. In case of medical diagnosis, apart from model performance other factors such as cost of data acquisition may also be taken into account. Since, models which are relatively expensive to operate would have diminished applicability in the field. Therefore, both performance and cost factors may be considered for designing the automated diagnosis solutions. In this regard, we have proposed two ensemble based cost-sensitive feature ranking techniques which tend to select an optimal feature subset by evaluating the cost-benefit trade-off on benchmarked chronic kidney disease dataset. We have also addressed the issue of automatic threshold selection which is generally faced by feature ranking approaches; hence enhancing the overall applicability of the solution. In this research, the main focus is on the application of decision-tree based classifiers in a cost-effective manner. As a case study, we have selected chronic kidney disease as the application of interest in which we demonstrate that our proposed approaches select features which are both useful and cost-effective. Furthermore, the proposed approaches are also evaluated against a number of comparative techniques and the results are promising.

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Section: Literature Reviewmentioning

confidence: 99%

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Ali¹,

Park²,

Lee³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In another study, the same framework is used as reported in [20], in which they used three classifiers on a CKD dataset which was acquired from Prince Hamza Hospital, Jordan. The study reported that the decision tree model performed reasonably well on a number of performance metrics [21]. N. Tazin et al [21] used several classification models such as SVM, Naive Bayes, KNN, and decision tree on the CKD dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study reported that the decision tree model performed reasonably well on a number of performance metrics [21]. N. Tazin et al [21] used several classification models such as SVM, Naive Bayes, KNN, and decision tree on the CKD dataset. Subsequently, a feature ranking is generated from which the top 10 features were selected.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Ali¹,

Ali²,

Hussain³

et al. 2020

Preprint

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Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

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“…Levels of creatinine, sodium, urea in blood play an important role in deciding the survival prediction or the need for kidney transplantation in patients undergoing dialysis and becoming worser .V. Ravindra.et al [8] used simple K-means algorithm to elicit knowledge about the interaction between many of these CKD parameters and patient survival. He concluded that the clustering procedure predicts the survival period of the patients who undergo the dialysis procedure.…”

Section: Literature Surveymentioning

confidence: 99%

Chronic Kidney Disease Prediction using Machine Learning Models

Revathy¹,

Bharathi²,

Jeyanthi³

et al. 2019

IJEAT

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The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing,data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD

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Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique

Cited by 42 publications

References 7 publications

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Cost-sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis

Chronic Kidney Disease Prediction using Machine Learning Models

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