Anemia Modelling Using the Multiple Regression Analysis

Sarı, Murat; Ahmad, Arshed A.

doi:10.28924/2291-8639-17-2019-838

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The data used for this study were collected from observations of blood variables and included 539 subjects provided from the literature [2,41]. Observations include individuals of the ages between (6-56) years old and 211 healthy subjects and 328 patients with anemia.…”

Section: Study Samplesmentioning

confidence: 99%

Medical model estimation with particle swarm optimization

Sarı¹,

Ahmad²,

Uslu³

2021

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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In this paper, a nonlinear medical model based on observational variables has been produced and the particle swarm optimization (PSO) technique, which is an e¤ective 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 …ndings, 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.

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Section: Study Samplesmentioning

confidence: 99%

Medical model estimation with particle swarm optimization

Sarı¹,

Ahmad²,

Uslu³

2021

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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Biomedical modelling through path analysis approach

Ahmad,

Sarı,

Demir

2024

Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.

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Since blood disease markers are one of the most prevalent health problems in this era, the aim of this study is to forecast pathological subjects from a population through biomedical variables of individuals using the currently produced path analysis (PA) model. In terms of the dataset, 539 subjects were used to implement this study. A mathematical approach based on the PA has been used to create a reliable biomedical model in this research that investigates if there exists a relation between the various anemia types and the biomedical variables through observational variables (the blood variables, age, and sex) and anemia types. Other linear approaches were taken into consideration for comparison, in terms of $R^2$ value of the model, which has a value of 0.699. The findings reveal that the model has great predictive potential. It is believed that the developed model, which includes observational variables, will help healthcare providers predictively plan appropriate treatment programs for their patients.

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Anemia Modelling Using the Multiple Regression Analysis

Cited by 2 publications

References 21 publications

Medical model estimation with particle swarm optimization

Medical model estimation with particle swarm optimization

Biomedical modelling through path analysis approach

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