Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach

Agung Prabowo; Sumita Wardani; Abdul Muis; Radiman Gea; Nathanael Atan Baskita Tarigan

doi:10.47709/cnahpc.v6i1.3611

Authors

Agung Prabowo Universitas Prima Indonesia
Sumita Wardani Universitas Prima Indonesia
Abdul Muis Universitas Satya Terra Bhinneka
Radiman Gea Universitas Prima Indonesia
Nathanael Atan Baskita Tarigan Universitas Prima Indonesia

DOI:

10.47709/cnahpc.v6i1.3611

Keywords:

Chronic Kidney Disease, Classification, Machine Learning, Majority Voting, Ensemble, Stacked Generalization, Support Vector Machine

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Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches

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