The aim of this article is to forecast anemia from a population through biomedical variables of individuals using the multiple linear regression model. The study is conducted in terms of dataset consisting of 539 subjects provided from blood laboratories. A multiple linear regression model is produced through biomedical information. To achieve this, a mathematical method based on multiple regression analysis has been applied in this research for a reliable model that investigate if there exists a relation between the anemia and the biomedical variables and to provide the more realistic one. For comparison purposes, the linear deep learning methods have also been considered and the current results are seen to be slightly better.The model based on the variables and outcomes is expected to serve as a good indicator of disease diagnosis for health providers and planning treatment schedules for their patients, especially predict of the type of anemia.
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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