Integrating Artificial Intelligence in Vital Statistics: Innovations in Public Health Data Analyses
Keywords:
Artificial Intelligence, Vital Statistics, Public Health, Data Analysis, Machine Learning, Predictive Analytics, Data Privacy, Real-time Processing
Abstract
Birth, death, marriage, and divorce statistics are essential for population studies and primary factors for public health strategy and evaluation. The application of Artificial Intelligence (AI) in evaluating and interpreting statistics significantly accelerates the process and enhances precision. Machine learning and deep learning, for example, can provide methods for big data analysis, filter out variance that would otherwise be confusing, and find correlations that otherwise would not have been seen with the naked eye or other analysis methods. Looking at the state of the topic of vital statistics analysis in the present, the problems of gaps in data, their quality, and relevance come to the foreground. It showcases AI solutions in data enrichment, live analysis, and inventive approaches such as NLP and predictive analysis. Using data from elements that define clinical conditions like blood pressure and heart rate, AI will give a better picture of population health. It enables monitoring of the onset of sicknesses such as hypertension. This paper employs case studies across different countries to explain how AI has helped enhance the quality of collected public health data and helping improve policies. It is recommended that future studies aim to eradicate the existing hurdles, including data aggregation from multiple sources and AI model bias, to harness AI potential in the sphere of public health.
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Published
2024-11-13
How to Cite
Lainjo, B. (2024). Integrating Artificial Intelligence in Vital Statistics: Innovations in Public Health Data Analyses. European Journal of Science, Innovation and Technology, 4(5), 184-198. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/548
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