Dual Kidney-Inspired Algorithm for Water Quality Prediction and Cancer Detection

Abstract

The kidney-inspired algorithm (KA) was presented in a recent research paper as a metaheuristic search algorithm. The KA imitates the physiological process of the kidneys in the human body. The second kidney in the human body filters all the solutes if the other kidney fails. If the second kidney also gets damaged, dialysis can be performed as a treatment. The failure of a kidney is proved by the Glomerular Filtration Rate (GFR) calculation. In this paper, a Dual-population Kidney-inspired Algorithm (Dual-KA), which contributes to the enhancement of a diversity of solutions and, subsequently, better exploration of the search space, is proposed, as a research objective, in the form of a novel simulation of cooperation between kidneys in human body. If GFR is greater than 60 in each iteration, the process in Dual-KA is continued as normal. If this number is between 15 and 60, some treatment is performed. Else, if the GFR is less than 15 and the current sub-population is the first subpopulation with a GFR of less than 15, all the solutions in the current sub-population are sent to the other sub-population in the current iteration. Otherwise, a dialysis or transplant process is carried out. Dual-KA is tested on 12 known test functions, standard classification and time series datasets. Water quality prediction and cancer detection are two real-world applications of this algorithm. The mentioned algorithm is run on these problems and its high ability is proved. INDEX TERMS Dual kidney-inspired algorithm; Artificial neural network; Water quality prediction; Cancer detection.

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