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A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery

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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.

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Data availability

The dataset used in this study is available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232)

Code availability

Self code. Available on request.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Silchar, NIT Road, Cachar, Silchar, Assam, 788010, India

    Zakir Hussain & Malaya Dutta Borah

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  1. Zakir Hussain
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  2. Malaya Dutta Borah
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Contributions

Conceptualization: Zakir Hussain; Methodology: Zakir Hussain; Formal analysis and investigation: Zakir Hussain; Writing - original draft preparation: Zakir Hussain; Writing - review and editing: Zakir Hussain, Malaya Dutta Borah; Supervision: Malaya Dutta Borah.

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Correspondence to Zakir Hussain.

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Hussain, Z., Borah, M.D. A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery. Health Inf Sci Syst 12, 10 (2024). https://doi.org/10.1007/s13755-024-00272-z

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