An Improved Prediction of Kidney Disease using SMOTE

Potharaju, Sai Prasad; Sreedevi, M.

doi:10.17485/ijst/2016/v9i31/95634

Cited by 15 publications

(11 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm found to improve the accuracy, and output is tested with other algorithms, such as support vector machines (SVM), artificial neural networks (ANN), decision trees, and classical Adaboost algorithm. In research article [13], worked on prediction of kidney disease using different rule based and tree based algorithms using SMOTE .In [14] ,the authors demonstrated an improvement of the precision of classification algorithm findings. Two different strategies bagging and boosting are used to increase the precision in their article.…”

Section: Related Workmentioning

confidence: 99%

Ensembled Rule Based Classification Algorithms for predicting Imbalanced Kidney Disease Data

Potharaju¹,

Sreedevi²

2016

JESTR

Self Cite

View full text Add to dashboard Cite

Imbalanced data is a type of data where there exists a difference in the ratio of classes. It occurs easily in real life of data analysis. In Data mining the functioning of learning algorithms caused by the imbalanced data. Most of the machine learning algorithms has a tendency to prejudice towards the class of majority in case of imbalanced data and hence those algorithms misjudge the minority class. Therefore, In this article we discuss a systematic way to address the imbalanced data classification problem by applying the rule based ensemble learning techniques like bagging, boosting, voting and stacking to build models, and then accelerates the performance of learning algorithms. In this research, we have preferred real data of chronic kidney disease which is collected from Appolo Hospitals, Tamil Nadu, India, to predict kidney disease of patients .The collected data is initially imbalanced. Firstly, the imbalanced data is balanced by applying SMOTE algorithm, which is an over sampling technique. Then applied various ensemble learning techniques to make better prediction. The incurred results showed that the model template chosen can minimize the problem of misclassification of imbalanced data efficaciously. But this model template cannot classify correctly when imbalanced rate of class increases i.e. in case of Big Data. For better result of imbalanced Big Data, new algorithmic plan of action has to be exploited which can be measured by using Hadoop framework and mapreduce programming model.

show abstract

Section: Related Workmentioning

confidence: 99%

Ensembled Rule Based Classification Algorithms for predicting Imbalanced Kidney Disease Data

Potharaju¹,

Sreedevi²

2016

JESTR

Self Cite

View full text Add to dashboard Cite

show abstract

“…The procedure minimizes the variations between the balanced and imbalanced dataset. An overall classification accuracy of 98.73% was reported [15] Koklu &Tutuniu made an attempt to clearly to classify CKD and NCKD using four data mining methods [16]. Though results were shown, no specific details of the algorithm was reported.…”

Section: Related Workmentioning

confidence: 99%

Classification of non-chronic and chronic kidney disease using SVM neural networks

Ravindra¹,

Sriraam

Geetha³

2017

IJET

View full text Add to dashboard Cite

Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.

show abstract

“…There are some literature existed to address these issue. SMOTE is an oversampling technique used to address the class imbalanced problem [4,5]. Authors of the research article [6] applied the SMOTE with ensembling approaches for increasing the prediction rate of kidney disease data.…”

Section: Introductionmentioning

confidence: 99%

“…Authors of the research article [6] applied the SMOTE with ensembling approaches for increasing the prediction rate of kidney disease data. Filter based feature selection algorithms have been applied by the researcher for the classification of SONAR signal data [4,8].Symmetrical Uncertainty based feature selection method have been applied over some of the medical dataset to derive the best features before applying classification algorithms [9,10]. Feature selection based on correlation coefficient and Symmetrical Uncertainty is proposed by the researchers and applied over various dataset belongs to diverse areas [11].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJET

View full text Add to dashboard Cite

Over the past two decades, the Internet has emerged as the mainstream medium for online shopping, social networking, e-mail and more. Corporations also view the Web as a potential business accelerator. They see the huge volume of transactional and interaction data generated by the Internet as research and development that informs the creation of new and more competitive services and products. The proposed system enable startup companies to track Clients interactions across services. This system allows to make open interaction between startup companies and their clients. In this system client ask for the solutions and multiple startup companies are allowed to give solutions. Depending on these solutions client will give feedback to startup companies. This feedbacks are then mined and recommendations are provided to startup companies. Also system allows visitors to view recommendations and solutions. This recommendations are helpful for startups to get more clients and they will get exposure.

show abstract

An Improved Prediction of Kidney Disease using SMOTE

Cited by 15 publications

References 3 publications

Ensembled Rule Based Classification Algorithms for predicting Imbalanced Kidney Disease Data

Ensembled Rule Based Classification Algorithms for predicting Imbalanced Kidney Disease Data

Classification of non-chronic and chronic kidney disease using SVM neural networks

Untitled

Contact Info

Product

Resources

About