Abstract
Purpose: This paper utilizes data mining to assist policy makers to better understand the overall health of communities in Chicago area using several public health indicators. The work utilizes regression analysis to establish relationships between social, economic and heath variables.
Design/Methodology/Approach: The main goal of the basic analysis was to identify several variables of interest for further investigation by multiple regression analysis. A correlation matrix from R was used to visualize associations between all independent variables and the dependent variables of interest for this study. The Akaike information criterion (AIC) value was then determined using backward variable selection. To classify the Chicago community areas according to similarities, k-means clustering was utilized in R. The data was transformed into a matrix and scaled.
Findings: The study found that socio-economic factors such as unemployment and crowded housing contribute to the increase in teen birth rate in Chicago community areas. This indicates that financial problems due to unemployment could lead to teenage pregnancies. The study reveals that assault, cancer, diabetes, and infant mortality all contribute to the increase in death rates in Chicago communities. In addition, unemployment and having no high school diploma is associated with communities being rated below the poverty line.
Research Limitations: The challenge in obtaining spatial statistics data for the communities was a major limitation. The spatial data affords a good way to do clustering and visualize the similarities in the communities.
Social Implications: For sustainable industrialization within the community, issues of public health are central. This will impliedly require health sector officials to focus more on the significant health issues and educate residents.
Originality: The innovation of the use of a simple data mining technique to assess the public health of communities in the Chicago area is unique to other methods employed in the literature.
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Akoto, D., Akoto, R.N.A. (2023). Public Health Predictive Analysis of Chicago Community Areas: A Data Mining Approach. In: Aigbavboa, C., et al. Sustainable Education and Development – Sustainable Industrialization and Innovation. ARCA 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-25998-2_16
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