Preventing Salmonella: Integrating Data Analytics With Electronic Health Records In Smart Cities

Farooq Aziz¹ , Shiv Kumar²

1. C.T Bauer College of Business, University of Houston, Houtson, Texas, USA.
2. Computer Science, Institute of Business Administration, Karachi, Pakistan.

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.5 , pp.1-10, May-2024

Online published on May 31, 2024

Copyright © Farooq Aziz, Shiv Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This study illuminates the intersection of Electronic Health Records (EHRs) and data analytics in managing salmonellosis outbreaks within smart cities. EHRs have evolved from basic information repositories to dynamic platforms integral for efficient healthcare administration. With a focus on data from the CDC, machine learning models, especially Random Forest and Gradient Boosting, were employed, achieving up to 100% predictive accuracy. However, this perfection in model performance necessitates caution and further validation to mitigate overfitting. Correlations emerged, such as the link between raw egg consumption and increased salmonellosis risk, highlighting the models` potential in real-world applications. Despite the evident strides in technology and data utilization, challenges in data security and privacy are still prominent. The insights derived are expected to bolster public health strategies and the integration of real-time data analytics in smart city health infrastructure, balancing technological innovation with ethical compliance.

Key-Words / Index Term :
electronic health records, data analytics, salmonellosis, machine learning, data security, smart cities, public health

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