A computational model to analyze the impact of birth weight-nutritional status pair on disease development and
Zakir Hussain1, Malaya Dutta Borah1
1Department of Computer Science and Engineering, National Institute of Technology Silchar, NIT Road, Cachar, Silchar, Assam 788010 India.
Insights
Optimal birth weight and nutrition are key for disease resistance and recovery. Low birth weight with malnutrition poses the highest risk for disease development and poor recovery outcomes.
Area of Science:
- Computational biology
- Health informatics
- Disease modeling
Background:
- Birth weight and nutritional status are critical determinants of an individual's health.
- Understanding their combined impact on disease is essential for public health strategies.
Purpose of the Study:
- To analyze the combined effects of birth weight and nutritional status on disease development and recovery.
- To computationally establish the impact of specific birth weight-nutritional status pairs on health outcomes.
Main Methods:
- Development of a two-phase computational model.
- Phase 1: Machine learning models (Gradient Boosting, SVM) used to predict birth weight from a public dataset.
- Phase 2: Integration of predicted birth weight with nutritional status data to analyze disease dynamics using differential equations.
Main Results:
- Gradient Boosting with Information Gain and SVM with Chi-square test were optimal for birth weight prediction.
- Normal birth weight and normal nutritional status showed the best outcomes for disease resistance and recovery.
- Low birth weight with malnutrition was worst for disease development; high birth weight with malnutrition was worst for recovery.
Conclusions:
- Computational analysis confirms the significant impact of birth weight-nutritional status pairs on disease development and recovery.
- Findings highlight the importance of adequate birth weight and nutrition for overall health.
- Results can inform public health policies and healthcare resource allocation.
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.
