Leveraging Data Science in Predicting and Preventing Child Health Issues
Keywords:
Data Science, Child Health, Disease Prevention, Predictive Analytics, Public HealthAbstract
Data science and analytics are revolutionizing healthcare by providing insights into disease prevention and early intervention. This paper investigates the use of data science in predicting and preventing child health issues, such as respiratory diseases, allergies, and infectious diseases. By analyzing large datasets from healthcare providers and tracking environmental factors, the paper demonstrates how data science can predict health trends, identify risk factors, and inform public health policies for children.
References
Anderson, M., & Lee, R. (2020). Using big data to predict child health issues: A review of predictive models. Pediatric Data Science Journal, 4(2), 100-107.
CDC. (2021). Predicting asthma risk using environmental data: A CDC study. Retrieved from https://www.cdc.gov/asthma
Cohen, D., & Mendez, A. (2019). Public health and predictive analytics: A case study in child health. Health Informatics Journal, 25(3), 441-448.
Kim, J., Lee, S., & Park, J. (2019). The role of predictive analytics in managing childhood infectious diseases. Journal of Infectious Diseases in Children, 45(5), 399-407.
Lee, H., & Chan, Y. (2021). Predictive algorithms in child allergy prevention: A systematic review. Journal of Allergy and Clinical Immunology, 138(2), 563-570.
Lee, K., & Young, A. (2021). Personalized health prediction for children: Insights from big data analytics. Journal of Personalized Medicine, 11(1), 47-55.
Liu, J., & Wang, D. (2020). Predictive models for infectious diseases in children: A global perspective. Global Health Review, 22(4), 120-128.
Patel, R., Khan, A., & Thomas, L. (2020). Using data science for predicting asthma attacks in children: A review. Journal of Pediatric Health Care, 34(4), 231-239.
Schneider, A., & Ma, G. (2020). Pediatric disease prediction: Leveraging data science for early intervention. Journal of Pediatric Medicine, 29(6), 215-220.
Sharma, P., & Singh, R. (2022). The potential of predictive analytics in pediatric healthcare: A case study approach. International Journal of Child Health, 32(1), 34-40.
Thompson, R., & Garcia, M. (2021). Data science for public health: A comprehensive review of predictive tools for child health issues. Journal of Public Health Informatics, 11(2), 87-94.
Williams, D., Jones, T., & Harrison, P. (2022). Data science and public health: Predictive analytics in childhood vaccination strategies. American Journal of Public Health, 112(7), 984-992.
World Health Organization (WHO). (2020). Predictive modeling in infectious disease prevention. WHO Technical Report Series, 102, 12-22.
Zhang, L., & Huang, X. (2019). The intersection of machine learning and pediatric healthcare: Applications and challenges. Journal of Pediatric Informatics, 18(3), 210-219.
Zhao, Y., & Wu, X. (2021). Machine learning algorithms in predicting pediatric respiratory conditions. Pediatric Pulmonology, 56(5), 1020-1029.
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