Medical model estimation with particle swarm optimization

Year 2021, Volume: 70 Issue: 1, 468 - 482, 30.06.2021

Murat Sarı Arshed Ahmad Hande Uslu

https://doi.org/10.31801/cfsuasmas.644071

Cited By: 1

Abstract

In this paper, a nonlinear medical model based on observational variables has been produced and the particle swarm optimization (PSO) technique, which is an effective technique to predict optimum parameters of the biomedical model, has been used. This study has been conducted on a dataset consisting of 539 subjects. For comparison purposes, nonlinear regression analysis, nonlinear deep learning, and nonlinear regression neural network methods are also considered and the PSO results appear to be slightly better than that of other methods. Built on observational variables and findings, the model is expected to be a good guide for healthcare professionals in diagnosing pathologies and planning treatment programs for their patients. It is therefore strongly believed that the article will be particularly useful for those interested in emerging biomedical models in various medical modelling areas such as infectious and hematological diseases such as anemia.

Keywords

Anemia, medical modelling, nonlinear model, particle swarm optimization

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