A computational model to analyze the impact of birth weight-nutritional status pair on disease development and disease recovery
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Health information science and systems - 12(2024), 1 vom: 14. Feb., Seite 10 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hussain, Zakir [VerfasserIn] |
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| Links: |
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| Themen: |
Birth weight |
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| Anmerkungen: |
Date Revised 21.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s13755-024-00272-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Birth weight | |
| 650 | 4 | |a Disease development | |
| 650 | 4 | |a Disease recovery | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Nutritional status | |
| 700 | 1 | |a Borah, Malaya Dutta |e verfasserin |4 aut | |
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