Regression Based Machine Learning to Generate and Validate a Metric for Food Insecurity
2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1788801
ABSTRACT
With the increased cost of living, exacerbated by COVID-19, thousands in New Jersey lack secure access to nutritious food. Given the importance of the issue, this research aims to produce an accurate metric using accessible data to quantify food insecurity. 16 potential explanatory variables, such as median household income and homeless population, were chosen as their values were defined across all NJ counties from 2015-2019. Using multiple linear regression, 14 unique metrics were created after four different variable pruning methods. The leading metric, with an adj. R2 value of 0.932, demonstrates the correlation between food insecurity, population, median household income, total population with health insurance, and population with private health insurance. The implementation of this metric could serve as a tool in predicting areas of food insecurity, highlighting affiliated factors, and revealing connections between racial populations. © 2021 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021
Year:
2021
Document Type:
Article
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