ABSTRACT
Purpose: The purpose of this work is to analyse the combined impacts of birth weight and nutritional status on development and recovery of various types of diseases. This work aims to computationally establish the facts about the effects of individual birth weight-nutritional status pairs on disease development and disease recovery. Methods: This work designs a computational model to analyze the impact of birth weight-nutritional status pairs on disease development and disease recovery. Our model works in two phases. The first phase finds the best machine learning model to predict birth weight from "Child Birth Weight Dataset" available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232). The second phase combines the predicted birth weight labels with nutritional status labels and establishes the effects using differential equations. Results: The experimental results find Gradient boosting (GB) to work the best with Information gain (IGT) and Support Vector Machine (SVM) with Chi-square test (CST) for predicting the birth weights. The simulated results establish that "normal birth weight and normal nutritional status" is the best pair for resisting disease development as well as enhancing disease recovery. The results also depict that "low birth weight and malnutrition" is the worst pair for disease development while "high birth weight and malnutrition" is the worst combination for disease recovery. Conclusion: The findings computationally establish the facts about the effects of birth weight-nutritional status pairs on disease development and disease recovery. As a social implication, this study can spread awareness about the importance of birth weight and nutritional status. The outcome can be helpful for the concerned authority in making decisions on healthcare cost and expenditure.
ABSTRACT
A few months back there was no medication and vaccine for COVID-19. Yet, most of the infected people got recovered. A very small portion of the infected people could not recover. A lion's share of the fatal cases were the patients suffering from some kind of chronic critical diseases. Due to that, their nutritional status and immunity were not normal. In this study, we have proposed a model called NICOV (Nutritional status, Immunity and COVID) that establishes the relationship among nutritional status, immunity, and COVID-19. This model formulates the relations considering all possible states of nutritional status and immunity of the body. We have numerically simulated the model for four different sets of values and found that susceptible, infected, and recovered cases of COVID-19 are significantly related to different states of nutritional status and immunity. It is also evident from numerical simulation that the effect of nutritional status and immunity varies with variation of other parameters associated with the formulation of the model. This model can help the concerned in decision making for mitigation of the losses that arise due to COVID-19-like situations.
