Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

Kumar*, C.Sathish; Thangaraju, P.

doi:10.35940/ijeat.f9377.088619

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The value of accuracy and the time required for classification are taken as the results of the study. This study states that the implementation results using SVM are superior to other classification methods [3] .…”

Section: Previous Researchesmentioning

confidence: 82%

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Qolby

Buliali

Saikhu

2021

JTS

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Chronic Kidney Disease (CKD) is a disorder that impairs kidney function. Early signs of CKD patients are very difficult until they lose 25% of their kidney function.Therefore, early detection and effective treatment are needed to reduce the mortality rate of CKD sufferers. In this study, the authors diagnose the CKD dataset using the Support Vector Machine (SVM) classification method to obtain accurate diagnostic results. The authors propose a comparison of the result on applying the feature selection method to get the best feature candidates in improving the classification result.The testing process compares the Symmetrical Uncertainty (SU) and Multivariate Symmetrical Uncertainty (MSU) feature selection method and the SVM method as a classification method. Several experimental scenarios were carried out using the SU and MSU feature selection methods using the CKD dataset. From the results of the tests carried out, it shows that using the MSU feature selection method with 80%: 20% data split produces nine important features with an accuracy value of 0.9, sensitivity 0.84, specification 1.0, and when viewed on the ROC graph, the MSU method graph shows the true positive value is higher than the false positive value. So the classification using the MSU feature selection method is better than the SU feature selection method by 90% accuracy.

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Section: Previous Researchesmentioning

confidence: 82%

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Qolby

Buliali

Saikhu

2021

JTS

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“…When a problem cannot be split linearly in the input space, a kernel function in SVM is used. The kernel function defined in equation (13).…”

Section: Svmmentioning

confidence: 99%

“…Kumar and Thangaraj [13] analyzed the CKD dataset obtained from the UCI machine learning repository. Pre-processing techniques such as missing value replacement, unsupervised discretion, and standardization were used to improve the accuracy.…”

mentioning

confidence: 99%

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Sharma¹,

Kaur²

2022

APJHS

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The massive generation of medical data from smart health-care applications in recent years necessitates the development of big data classification strategies. Medical data classification can be used to visualize patterns in the data and detect the presence of the disease in medical data. We present an efficient support vector machine (SVM) hybridized with a grey wolf optimization (GWO) algorithm for chronic kidney disease (CKD) data classification in this work. Initially, infinite feature selection (IFS) algorithm is used to select the best features from a set of available features. The dataset’s selected features are processed and fed into the GWO optimized SVM algorithm. The proposed CKD classification strategy has been simulated in MATLAB. CKD dataset from UCI machine learning repository is utilized for testing the developed strategy. The performance of the proposed CKD classification strategy is examined by accuracy and root mean square error (RMSE) values. According to the investigational findings, the proposed CKD classification system achieved accuracy and RMSE value of 97.58% and 0.1581, respectively, for classifying subjects into the CKD and non-CKD categories. The performance of GWO optimized SVM algorithm is outstanding, according to the experimental observations.

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Analysis to Improve Classifier Accuracy in Crime Data Prediction

Menaka

Booba

2022

2022 6th International Conference on Computing Methodologies and Communication (ICCMC)

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Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

Cited by 3 publications

References 13 publications

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Analysis to Improve Classifier Accuracy in Crime Data Prediction

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