Anemia Prediction with Multiple Regression Support in System Medicinal Internet of Things

Abstract

Since anemia is one of the most common health problems in this era, the aim of this paper is to forecast pathological subjects from a population through biomedical variables of individuals using the currently produced multiple nonlinear regression model. This work has been carried out in terms of the dataset consisting of 539 subjects provided from blood laboratories. 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. The nonlinear regression model has been produced through biomedical information, observational variables (the blood variables, age, and sex) and the types of anemia. The parameter values produced are all seen to be the optimum values obtained from the multiple regression approaches, to provide the more realistic one. The findings reveal that the multiple regression model has the potential to predict anemia. In this respect, these results justify once again that the Medicinal Internet of Things (MIoT) is of great importance in health-related practical fields today. Thus the MIoT in the current system is expected to improve standards of health-providing and living through individual data-driven treatment plans as well as optimized treatment programs designed to individual biomedical needs.