Kidney-inspired algorithm with reduced functionality treatment for classification and time series prediction

Najmeh Sadat Jaddi, Salwani Abdullah

Research output: Contribution to journalArticle

Abstract

Optimization of an artificial neural network model through the use of optimization algorithms is the common method employed to search for an optimum solution for a broad variety of real-world problems. One such optimization algorithm is the kidney-inspired algorithm (KA) which has recently been proposed in the literature. The algorithm mimics the four processes performed by the kidneys: filtration, reabsorption, secretion, and excretion. However, a human with reduced kidney function needs to undergo additional treatment to improve kidney performance. In the medical field, the glomerular filtration rate (GFR) test is used to check the health of kidneys. The test estimates the amount of blood that passes through the glomeruli each minute. In this paper, we mimic this kidney function test and the GFR result is used to select a suitable step to add to the basic KA process. This novel imitation is designed for both minimization and maximization problems. In the proposed method, depends on GFR test result which is less than 15 or falls between 15 and 60 or is more than 60 a particular action is performed. These additional processes are applied as required with the aim of improving exploration of the search space and increasing the likelihood of the KA finding the optimum solution. The proposed method is tested on test functions and its results are compared with those of the basic KA. Its performance on benchmark classification and time series prediction problems is also examined and compared with that of other available methods in the literature. In addition, the proposed method is applied to a real-world water quality prediction problem. The statistical analysis of all these applications showed that the proposed method had a ability to improve the optimization outcome.

Original languageEnglish
Article numbere0208308
JournalPLoS One
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

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Time series
time series analysis
kidneys
Kidney
prediction
glomerular filtration rate
Glomerular Filtration Rate
renal function
testing
methodology
Kidney Function Tests
Benchmarking
Space Flight
Neural Networks (Computer)
Water Quality
neural networks
Water quality
Statistical methods
Blood
statistical analysis

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Kidney-inspired algorithm with reduced functionality treatment for classification and time series prediction. / Jaddi, Najmeh Sadat; Abdullah, Salwani.

In: PLoS One, Vol. 14, No. 1, e0208308, 01.01.2019.

Research output: Contribution to journalArticle

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