Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019401" target="_blank" >RIV/62690094:18470/22:50019401 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/csse/v42n3/46719" target="_blank" >https://www.techscience.com/csse/v42n3/46719</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/csse.2022.021784" target="_blank" >10.32604/csse.2022.021784</a>

Result language

angličtina

Original language name

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Original language description

Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated using ensemble nonlinear support vector machines and random forests. Thus, instead of using the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vector machines, where the product rule is used to combine probability estimates of different classifiers. Performance is evaluated in terms of the prediction accuracy and interpretability of the model and the results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computer Systems Science and Engineering

ISSN

0267-6192

e-ISSN

—

Volume of the periodical

42

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

1273-1287

UT code for WoS article

000759543900009

EID of the result in the Scopus database

2-s2.0-85124974608